Patients diagnosed with PD in 2016: transition matrices

The database contains information on 30423 patients aged 50 and over. It includes both patients diagnosed with Parkinson’s disease (PD) in 2020 and living patients who received an earlier diagnosis. The criterion used to distinguish between the two stages of PD is hospitalisation: if a patient has been hospitalised due to PD, then they are considered to have severe PD. The variables are as follows:

A filter to keep only the new diagnoses in 2016 has been applied in accordance with the gender/age class/year of diagnosis approach.

library(readxl)
library(tidyverse)
library(purrr)
library(janitor)
library(ggplot2)

df <- read_excel("Parkinson data n_patients.xlsx")
f_prob <- read_excel("Prob of death.xlsx", sheet = "f prob" )

Now let’s break down data with respect to:

library(dplyr)
library(tidyverse)
#Filter to retain new diagnoses in 2016
summary_df <- df %>% 
   filter( 
      first_year == "2016")%>% mutate(
    yod_binary = case_when(
      yod != "Alive" ~ "Dead",
      TRUE ~ yod 
    ),
    gender = factor(BEN_SEX_COD, levels = c("1", "2"), labels = c("Male", "Female"))
  ) %>% 
  group_by(CLA_AGE_5, severity, severity_at_end, gender, yod_binary) %>% 
  summarise(
    n_patients = sum(`n_patients`)
  ) %>% 
  ungroup() %>% 
  complete(CLA_AGE_5, severity, severity_at_end, gender, yod_binary, fill = list(n_patients = 0)) %>%
  select(CLA_AGE_5, gender, yod_binary, severity, severity_at_end, n_patients) %>% 
  arrange(gender)

summary_df
summary_df1 <- summary_df %>% 
  mutate(
    severity = case_when(
      severity == "Transitioned" & yod_binary == "Dead" ~ "Severe",
      TRUE ~ severity
    )
  ) %>% 
  #filter(severity != "Transitioned" & yod_binary != "Dead") %>% 
  group_by(CLA_AGE_5, severity, severity_at_end, gender, yod_binary) %>% 
  summarise(
    n_patients = sum(`n_patients`)
  ) %>% 
  ungroup() %>% 
  complete(CLA_AGE_5, severity, severity_at_end, gender, yod_binary, fill = list(n_patients = 0)) %>%
  select(CLA_AGE_5, gender, yod_binary, severity, severity_at_end, n_patients) %>% 
  arrange(gender)
  
summary_df1
#summary_df %>% write_excel_csv(file = "summary_df.csv")

summary_df2 <- df %>% 
 mutate(
    yod_binary = case_when(
      yod != "Alive" ~ "Dead",
      TRUE ~ yod 
    ),
    gender = factor(BEN_SEX_COD, levels = c("1", "2"), labels = c("Male", "Female"))
  ) %>% 
  group_by(CLA_AGE_5, severity, severity_at_end, gender, yod_binary) %>% 
  summarise(
    n_patients = sum(`n_patients`)
  ) %>% 
  ungroup() %>% 
  complete(CLA_AGE_5, severity, severity_at_end, gender, yod_binary, fill = list(n_patients = 0)) %>%
  select(CLA_AGE_5, gender, yod_binary, severity, severity_at_end, n_patients) %>% 
  arrange(gender)
summary_df2
sum(summary_df$n_patients)
## [1] 25525

The above table allows to build cohorts, where each cohort is identified by 4 consecutive rows. Now we can compute the transition matrix of each cohort:

The table f_prob estimates F according to the above descripted approach:

f_prob

The remaining elements are:

Generally speaking, a transition matrix with 4 states (Prodromal, Mild Parkinson Disease, Severe/Advanced Parkinson Disease, Death) looks like the following:

a <- matrix(NA, nrow = 4, ncol = 4)

a[1, 1] <- 0
a[1, 2] <- "1 - F"
a[1, 3] <- 0
a[1, 4] <- "F"
a[2, 1] <- 0
a[2, 2] <- "1 - P(MPD -> APD) - P(MPD -> D)" 
a[2, 3] <- "P(MPD -> APD)"
a[2, 4] <- "P(MPD -> D)"
a[3, 1] <- 0
a[3, 2] <- 0
a[3, 3] <- "1 - P(APD -> D)"
a[3, 4] <- "P(APD -> D)"
a[4, 1] <- 0
a[4, 2] <- 0
a[4, 3] <- 0
a[4,4] <- 1

a
##      [,1] [,2]                              [,3]              [,4]         
## [1,] "0"  "1 - F"                           "0"               "F"          
## [2,] "0"  "1 - P(MPD -> APD) - P(MPD -> D)" "P(MPD -> APD)"   "P(MPD -> D)"
## [3,] "0"  "0"                               "1 - P(APD -> D)" "P(APD -> D)"
## [4,] "0"  "0"                               "0"               "1"

The transition matrix for the first cohort (males within the 50-54 age class) is:

x <- matrix(NA, nrow = 4, ncol = 4)

x[1, 1] <- 0
x[1, 2] <- 1 - f_prob$F[1]
x[1, 3] <- 0
x[1, 4] <- f_prob$F[1]
x[2, 1] <- 0

numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_MPD <- summary_df %>%
    filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity == "Mild" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)


x[2, 3] <- numerator_MPD_APD / denominator_MPD
x[2, 4] <- numerator_MPD_D / denominator_MPD
x[2, 2] <- 1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD)

x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
  filter(CLA_AGE_5 == "50-54", gender == "Male", severity_at_end == "Severe", yod_binary == "Dead") %>%
  summarise(n_patients = sum(n_patients)) %>% 
  pull(n_patients)
denominator_APD_D <- summary_df2 %>%
  filter(CLA_AGE_5 == "50-54", gender == "Male", severity_at_end == "Severe") %>% 
  summarise(n_patients = sum(n_patients)) %>% 
  pull(n_patients)
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4,4] <- 1

x
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9712352 0.0000000 0.02876483
## [2,]    0 0.8423077 0.1076923 0.05000000
## [3,]    0 0.0000000 0.9291339 0.07086614
## [4,]    0 0.0000000 0.0000000 1.00000000

Let’s iterate the process for each of the 20 cohorts within the database:

age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")

generate_transition_matrix_old <- function(summary_df, summary_df2, age_classes, gender_name) {
  
  x <- matrix(NA, nrow = 4, ncol = 4)

  x[1, 1] <- 0
  f_prob1 <- f_prob %>% 
    filter(`Age class` == age_class, Gender == gender_name) %>% 
    summarise(f_prob = F) %>% 
    pull(f_prob)
  x[1, 2] <- 1 - f_prob1
  x[1, 3] <- 0
  x[1, 4] <- f_prob1

  numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)
  
  x[2, 1] <- 0
  x[2, 3] <- numerator_MPD_APD / denominator_MPD
  x[2, 4] <- numerator_MPD_D / denominator_MPD
  x[2, 2] <- 1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD)

  x[3, 1] <- 0
  x[3, 2] <- 0
  numerator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  denominator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  
  x[3, 4] <- numerator_APD_D / denominator_APD_D

  x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)

  x[4, 1] <- 0
  x[4, 2] <- 0
  x[4, 3] <- 0
  x[4, 4] <- 1

  return(x)
}

transition_matrices_old <- list()

for (gender in genders) {
  for (age_class in age_classes) {
    matrix_name <- paste(gender, age_class, sep = "_")
    transition_matrices_old[[matrix_name]] <- generate_transition_matrix_old(summary_df, summary_df2, age_class, gender)
  }
}


transition_matrices_old
## $`Male_50-54`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9712352 0.0000000 0.02876483
## [2,]    0 0.8423077 0.1076923 0.05000000
## [3,]    0 0.0000000 0.9291339 0.07086614
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Male_55-59`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9574518 0.00000000 0.04254822
## [2,]    0 0.8469388 0.08367347 0.06938776
## [3,]    0 0.0000000 0.87280702 0.12719298
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_60-64`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9433756 0.0000000 0.05662437
## [2,]    0 0.8275000 0.0675000 0.10500000
## [3,]    0 0.0000000 0.8191489 0.18085106
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Male_65-69`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9224868 0.00000000 0.07751319
## [2,]    0 0.7518892 0.06801008 0.18010076
## [3,]    0 0.0000000 0.69558600 0.30441400
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_70-74`
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.8875735 0.0000000 0.1124265
## [2,]    0 0.7059757 0.0560550 0.2379693
## [3,]    0 0.0000000 0.5703704 0.4296296
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## $`Male_75-79`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8201575 0.00000000 0.1798425
## [2,]    0 0.6240631 0.04970414 0.3262327
## [3,]    0 0.0000000 0.48199768 0.5180023
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_80-84`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.7046099 0.00000000 0.2953901
## [2,]    0 0.5081301 0.03399852 0.4578714
## [3,]    0 0.0000000 0.33866995 0.6613300
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_85-89`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.5279737 0.00000000 0.4720263
## [2,]    0 0.3530405 0.02083333 0.6261261
## [3,]    0 0.0000000 0.25708502 0.7429150
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_90-94`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.3260733 0.00000000 0.6739267
## [2,]    0 0.2357595 0.01107595 0.7531646
## [3,]    0 0.0000000 0.16030534 0.8396947
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_95et+`
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.1585850 0.0000000 0.8414150
## [2,]    0 0.1511628 0.0000000 0.8488372
## [3,]    0 0.0000000 0.1111111 0.8888889
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## $`Female_50-54`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9864538 0.0000000 0.01354618
## [2,]    0 0.9042904 0.0660066 0.02970297
## [3,]    0 0.0000000 0.9193548 0.08064516
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Female_55-59`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9814785 0.00000000 0.01852146
## [2,]    0 0.9093023 0.03953488 0.05116279
## [3,]    0 0.0000000 0.86885246 0.13114754
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_60-64`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9750718 0.00000000 0.02492824
## [2,]    0 0.8920455 0.05965909 0.04829545
## [3,]    0 0.0000000 0.85654008 0.14345992
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_65-69`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9644648 0.00000000 0.03553525
## [2,]    0 0.8446281 0.04793388 0.10743802
## [3,]    0 0.0000000 0.77889447 0.22110553
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_70-74`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9455591 0.00000000 0.05444087
## [2,]    0 0.7926174 0.05838926 0.14899329
## [3,]    0 0.0000000 0.71125265 0.28874735
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_75-79`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9040836 0.0000000 0.09591642
## [2,]    0 0.7200000 0.0505000 0.22950000
## [3,]    0 0.0000000 0.6180982 0.38190184
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Female_80-84`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8160931 0.00000000 0.1839069
## [2,]    0 0.6313457 0.03384367 0.3348106
## [3,]    0 0.0000000 0.48024316 0.5197568
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Female_85-89`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.6559712 0.00000000 0.3440288
## [2,]    0 0.4962816 0.01883986 0.4848785
## [3,]    0 0.0000000 0.37564767 0.6243523
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Female_90-94`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.4385294 0.000000000 0.5614706
## [2,]    0 0.3344867 0.005767013 0.6597463
## [3,]    0 0.0000000 0.268041237 0.7319588
## [4,]    0 0.0000000 0.000000000 1.0000000
## 
## $`Female_95et+`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.2311448 0.000000000 0.7688552
## [2,]    0 0.2912088 0.005494505 0.7032967
## [3,]    0 0.0000000 0.222222222 0.7777778
## [4,]    0 0.0000000 0.000000000 1.0000000
names(transition_matrices_old) <- NULL  

males_old <- transition_matrices_old[1:10]
females_old <- transition_matrices_old[11:20]

matrices_mf_old <- list(males_old, females_old)
matrices_mf_old
## [[1]]
## [[1]][[1]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9712352 0.0000000 0.02876483
## [2,]    0 0.8423077 0.1076923 0.05000000
## [3,]    0 0.0000000 0.9291339 0.07086614
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[2]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9574518 0.00000000 0.04254822
## [2,]    0 0.8469388 0.08367347 0.06938776
## [3,]    0 0.0000000 0.87280702 0.12719298
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[3]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9433756 0.0000000 0.05662437
## [2,]    0 0.8275000 0.0675000 0.10500000
## [3,]    0 0.0000000 0.8191489 0.18085106
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[4]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9224868 0.00000000 0.07751319
## [2,]    0 0.7518892 0.06801008 0.18010076
## [3,]    0 0.0000000 0.69558600 0.30441400
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[5]]
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.8875735 0.0000000 0.1124265
## [2,]    0 0.7059757 0.0560550 0.2379693
## [3,]    0 0.0000000 0.5703704 0.4296296
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## [[1]][[6]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8201575 0.00000000 0.1798425
## [2,]    0 0.6240631 0.04970414 0.3262327
## [3,]    0 0.0000000 0.48199768 0.5180023
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[7]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.7046099 0.00000000 0.2953901
## [2,]    0 0.5081301 0.03399852 0.4578714
## [3,]    0 0.0000000 0.33866995 0.6613300
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[8]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.5279737 0.00000000 0.4720263
## [2,]    0 0.3530405 0.02083333 0.6261261
## [3,]    0 0.0000000 0.25708502 0.7429150
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[9]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.3260733 0.00000000 0.6739267
## [2,]    0 0.2357595 0.01107595 0.7531646
## [3,]    0 0.0000000 0.16030534 0.8396947
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[10]]
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.1585850 0.0000000 0.8414150
## [2,]    0 0.1511628 0.0000000 0.8488372
## [3,]    0 0.0000000 0.1111111 0.8888889
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## 
## [[2]]
## [[2]][[1]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9864538 0.0000000 0.01354618
## [2,]    0 0.9042904 0.0660066 0.02970297
## [3,]    0 0.0000000 0.9193548 0.08064516
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[2]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9814785 0.00000000 0.01852146
## [2,]    0 0.9093023 0.03953488 0.05116279
## [3,]    0 0.0000000 0.86885246 0.13114754
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[3]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9750718 0.00000000 0.02492824
## [2,]    0 0.8920455 0.05965909 0.04829545
## [3,]    0 0.0000000 0.85654008 0.14345992
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[4]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9644648 0.00000000 0.03553525
## [2,]    0 0.8446281 0.04793388 0.10743802
## [3,]    0 0.0000000 0.77889447 0.22110553
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[5]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9455591 0.00000000 0.05444087
## [2,]    0 0.7926174 0.05838926 0.14899329
## [3,]    0 0.0000000 0.71125265 0.28874735
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[6]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9040836 0.0000000 0.09591642
## [2,]    0 0.7200000 0.0505000 0.22950000
## [3,]    0 0.0000000 0.6180982 0.38190184
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[7]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8160931 0.00000000 0.1839069
## [2,]    0 0.6313457 0.03384367 0.3348106
## [3,]    0 0.0000000 0.48024316 0.5197568
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[8]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.6559712 0.00000000 0.3440288
## [2,]    0 0.4962816 0.01883986 0.4848785
## [3,]    0 0.0000000 0.37564767 0.6243523
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[9]]
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.4385294 0.000000000 0.5614706
## [2,]    0 0.3344867 0.005767013 0.6597463
## [3,]    0 0.0000000 0.268041237 0.7319588
## [4,]    0 0.0000000 0.000000000 1.0000000
## 
## [[2]][[10]]
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.2311448 0.000000000 0.7688552
## [2,]    0 0.2912088 0.005494505 0.7032967
## [3,]    0 0.0000000 0.222222222 0.7777778
## [4,]    0 0.0000000 0.000000000 1.0000000
for (i in 1:length(males_old)) {
  colnames(males_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
  rownames(males_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
}
for (i in 1:length(females_old)) {
  colnames(females_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males_old)) {
  dimnames(males_old[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females_old)) {
  dimnames(females_old[[i]]) <- list(row_names_f, col_names_f)
}

transition_matrices_mf_old <- list(males_old, females_old)
transition_matrices_mf_old
## [[1]]
## [[1]][[1]]
##       P.m     MPD.m     APD.m        D.m
## P.m     0 0.9712352 0.0000000 0.02876483
## MPD.m   0 0.8423077 0.1076923 0.05000000
## APD.m   0 0.0000000 0.9291339 0.07086614
## D.m     0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[2]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9574518 0.00000000 0.04254822
## MPD.m   0 0.8469388 0.08367347 0.06938776
## APD.m   0 0.0000000 0.87280702 0.12719298
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[3]]
##       P.m     MPD.m     APD.m        D.m
## P.m     0 0.9433756 0.0000000 0.05662437
## MPD.m   0 0.8275000 0.0675000 0.10500000
## APD.m   0 0.0000000 0.8191489 0.18085106
## D.m     0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[4]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9224868 0.00000000 0.07751319
## MPD.m   0 0.7518892 0.06801008 0.18010076
## APD.m   0 0.0000000 0.69558600 0.30441400
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[5]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.8875735 0.0000000 0.1124265
## MPD.m   0 0.7059757 0.0560550 0.2379693
## APD.m   0 0.0000000 0.5703704 0.4296296
## D.m     0 0.0000000 0.0000000 1.0000000
## 
## [[1]][[6]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8201575 0.00000000 0.1798425
## MPD.m   0 0.6240631 0.04970414 0.3262327
## APD.m   0 0.0000000 0.48199768 0.5180023
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[7]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.7046099 0.00000000 0.2953901
## MPD.m   0 0.5081301 0.03399852 0.4578714
## APD.m   0 0.0000000 0.33866995 0.6613300
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[8]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.5279737 0.00000000 0.4720263
## MPD.m   0 0.3530405 0.02083333 0.6261261
## APD.m   0 0.0000000 0.25708502 0.7429150
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[9]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.3260733 0.00000000 0.6739267
## MPD.m   0 0.2357595 0.01107595 0.7531646
## APD.m   0 0.0000000 0.16030534 0.8396947
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[10]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.1585850 0.0000000 0.8414150
## MPD.m   0 0.1511628 0.0000000 0.8488372
## APD.m   0 0.0000000 0.1111111 0.8888889
## D.m     0 0.0000000 0.0000000 1.0000000
## 
## 
## [[2]]
## [[2]][[1]]
##       P.f     MPD.f     APD.f        D.f
## P.f     0 0.9864538 0.0000000 0.01354618
## MPD.f   0 0.9042904 0.0660066 0.02970297
## APD.f   0 0.0000000 0.9193548 0.08064516
## D.f     0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[2]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9814785 0.00000000 0.01852146
## MPD.f   0 0.9093023 0.03953488 0.05116279
## APD.f   0 0.0000000 0.86885246 0.13114754
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[3]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9750718 0.00000000 0.02492824
## MPD.f   0 0.8920455 0.05965909 0.04829545
## APD.f   0 0.0000000 0.85654008 0.14345992
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[4]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9644648 0.00000000 0.03553525
## MPD.f   0 0.8446281 0.04793388 0.10743802
## APD.f   0 0.0000000 0.77889447 0.22110553
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[5]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9455591 0.00000000 0.05444087
## MPD.f   0 0.7926174 0.05838926 0.14899329
## APD.f   0 0.0000000 0.71125265 0.28874735
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[6]]
##       P.f     MPD.f     APD.f        D.f
## P.f     0 0.9040836 0.0000000 0.09591642
## MPD.f   0 0.7200000 0.0505000 0.22950000
## APD.f   0 0.0000000 0.6180982 0.38190184
## D.f     0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[7]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.8160931 0.00000000 0.1839069
## MPD.f   0 0.6313457 0.03384367 0.3348106
## APD.f   0 0.0000000 0.48024316 0.5197568
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[8]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.6559712 0.00000000 0.3440288
## MPD.f   0 0.4962816 0.01883986 0.4848785
## APD.f   0 0.0000000 0.37564767 0.6243523
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[9]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.4385294 0.000000000 0.5614706
## MPD.f   0 0.3344867 0.005767013 0.6597463
## APD.f   0 0.0000000 0.268041237 0.7319588
## D.f     0 0.0000000 0.000000000 1.0000000
## 
## [[2]][[10]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.2311448 0.000000000 0.7688552
## MPD.f   0 0.2912088 0.005494505 0.7032967
## APD.f   0 0.0000000 0.222222222 0.7777778
## D.f     0 0.0000000 0.000000000 1.0000000
transition_matrices_m_old <- transition_matrices_mf_old[[1]]
transition_matrices_f_old <- transition_matrices_mf_old[[2]]

extract_rows_as_named_list <- function(matrix) {
  list(
    P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
    MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
    APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
    D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
  )
}

transition_prob_m_old <- lapply(transition_matrices_m_old, extract_rows_as_named_list)

transition_prob_f_old <- lapply(transition_matrices_f_old, extract_rows_as_named_list)

print("Transition Probabilities for Males:")
## [1] "Transition Probabilities for Males:"
print(transition_prob_m_old)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8423077 0.1076923 0.0500000 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.84693878 0.08367347 0.06938776 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##      P    MPD    APD      D 
## 0.0000 0.8275 0.0675 0.1050 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.75188917 0.06801008 0.18010076 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.7059757 0.0560550 0.2379693 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.62406312 0.04970414 0.32623274 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.50813008 0.03399852 0.45787140 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.35304054 0.02083333 0.62612613 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.23575949 0.01107595 0.75316456 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##        P      MPD      APD        D 
## 0.000000 0.158585 0.000000 0.841415 
## 
## [[10]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.1511628 0.0000000 0.8488372 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Transition Probabilities for Females:")
## [1] "Transition Probabilities for Females:"
print(transition_prob_f_old)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.90429043 0.06600660 0.02970297 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.90930233 0.03953488 0.05116279 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.89204545 0.05965909 0.04829545 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.84462810 0.04793388 0.10743802 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.79261745 0.05838926 0.14899329 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##      P    MPD    APD      D 
## 0.0000 0.7200 0.0505 0.2295 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.63134569 0.03384367 0.33481064 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.49628161 0.01883986 0.48487853 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.334486736 0.005767013 0.659746251 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2311448 0.0000000 0.7688552 
## 
## [[10]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.291208791 0.005494505 0.703296703 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1

Let’s analyse the graph depicting probabilities of death with respect to severity:

severity_labels <- c("Prodromal", "Mild", "Advanced")

# Extracting probabilities of death from matrices
extract_probabilities <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[1],
      probability_of_death = matrix[1, 4]
    ))
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_death = matrix[2, 4]
    ))
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[3],
      probability_of_death = matrix[3, 4]
    ))
  }
  
  return(data)
}

# Extracting data for males/females
males_data <- extract_probabilities(males_old, age_classes, "Male")
females_data <- extract_probabilities(females_old, age_classes, "Female")

final_data <- rbind(males_data, females_data)

graph_prob_mf <- ggplot(final_data, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
  theme_minimal() +
  labs(title = "Probability of death with respect to severity, baseline scenario",
       x = "Age class",
       y = "Probability",
       color = "Severity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf

A sensible relationship between probability of death and severity emerges from the graph: the more severe the disease, the higher the probability of death. The only exception is represented by the last age class due to the very small sample size.

Let’s impose this relationship as valid also for patients aged 95 years and more: the differential between the probability of dying when mild and the probability of dying when prodromal of the previous age class, 90-94, is used to adjust the last probability of dying when prodromal:

# Let's apply the adjustment
final_data1 <- final_data %>%
  group_by(gender) %>% 
  mutate(probability_of_death = ifelse(
    age_class == "95et+" & severity == "Prodromal",
    probability_of_death[age_class == "95et+" & severity == "Mild"] -
      (probability_of_death[age_class == "90-94" & severity == "Mild"] -
       probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
    probability_of_death
  ))

#final_data_males <- final_data1 %>%
 # filter(gender == "Male")

#final_data_females <- final_data1 %>% 
 # filter(gender == "Female")

age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")

f_prob1 <- f_prob %>%
  mutate(
    F = case_when(
      `Age class` == "95et+" & Gender == "Male" ~ final_data1 %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      `Age class` == "95et+" & Gender == "Female" ~ final_data1 %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      TRUE ~ F
    )
  )


generate_transition_matrix <- function(summary_df, summary_df2, final_data1, age_classes, gender_name) {
  
  x <- matrix(NA, nrow = 4, ncol = 4)
  x[1, 1] <- 0
  age_classes_to_select <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94")
  
  f_prob2 <- f_prob1 %>% 
    filter(`Age class` == age_classes & Gender == gender_name) %>% 
    pull(F)
   
  x[1, 2] <- 1 - f_prob2
  x[1, 3] <- 0
  x[1, 4] <- f_prob2

  x[2, 1] <- 0
  
  numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)
  
  x[2, 3] <- numerator_MPD_APD / denominator_MPD

  x[2, 4] <- numerator_MPD_D / denominator_MPD

  x[2, 2] <- 1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD)

  x[3, 1] <- 0
  x[3, 2] <- 0
  numerator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  
  denominator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  
  x[3, 4] <- numerator_APD_D / denominator_APD_D

  x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)

  x[4, 1] <- 0
  x[4, 2] <- 0
  x[4, 3] <- 0
  x[4, 4] <- 1

  return(x)
}

transition_matrices <- list()

for (gender in genders) {
  for (age_class in age_classes) {
    matrix_name <- paste(gender, age_class, sep = "_")
    transition_matrices[[matrix_name]] <- generate_transition_matrix(summary_df, summary_df2, final_data1, age_class, gender)
  }
}


transition_matrices
## $`Male_50-54`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9712352 0.0000000 0.02876483
## [2,]    0 0.8423077 0.1076923 0.05000000
## [3,]    0 0.0000000 0.9291339 0.07086614
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Male_55-59`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9574518 0.00000000 0.04254822
## [2,]    0 0.8469388 0.08367347 0.06938776
## [3,]    0 0.0000000 0.87280702 0.12719298
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_60-64`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9433756 0.0000000 0.05662437
## [2,]    0 0.8275000 0.0675000 0.10500000
## [3,]    0 0.0000000 0.8191489 0.18085106
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Male_65-69`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9224868 0.00000000 0.07751319
## [2,]    0 0.7518892 0.06801008 0.18010076
## [3,]    0 0.0000000 0.69558600 0.30441400
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_70-74`
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.8875735 0.0000000 0.1124265
## [2,]    0 0.7059757 0.0560550 0.2379693
## [3,]    0 0.0000000 0.5703704 0.4296296
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## $`Male_75-79`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8201575 0.00000000 0.1798425
## [2,]    0 0.6240631 0.04970414 0.3262327
## [3,]    0 0.0000000 0.48199768 0.5180023
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_80-84`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.7046099 0.00000000 0.2953901
## [2,]    0 0.5081301 0.03399852 0.4578714
## [3,]    0 0.0000000 0.33866995 0.6613300
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_85-89`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.5279737 0.00000000 0.4720263
## [2,]    0 0.3530405 0.02083333 0.6261261
## [3,]    0 0.0000000 0.25708502 0.7429150
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_90-94`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.3260733 0.00000000 0.6739267
## [2,]    0 0.2357595 0.01107595 0.7531646
## [3,]    0 0.0000000 0.16030534 0.8396947
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Male_95et+`
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.2304007 0.0000000 0.7695993
## [2,]    0 0.1511628 0.0000000 0.8488372
## [3,]    0 0.0000000 0.1111111 0.8888889
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## $`Female_50-54`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9864538 0.0000000 0.01354618
## [2,]    0 0.9042904 0.0660066 0.02970297
## [3,]    0 0.0000000 0.9193548 0.08064516
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Female_55-59`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9814785 0.00000000 0.01852146
## [2,]    0 0.9093023 0.03953488 0.05116279
## [3,]    0 0.0000000 0.86885246 0.13114754
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_60-64`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9750718 0.00000000 0.02492824
## [2,]    0 0.8920455 0.05965909 0.04829545
## [3,]    0 0.0000000 0.85654008 0.14345992
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_65-69`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9644648 0.00000000 0.03553525
## [2,]    0 0.8446281 0.04793388 0.10743802
## [3,]    0 0.0000000 0.77889447 0.22110553
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_70-74`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9455591 0.00000000 0.05444087
## [2,]    0 0.7926174 0.05838926 0.14899329
## [3,]    0 0.0000000 0.71125265 0.28874735
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_75-79`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9040836 0.0000000 0.09591642
## [2,]    0 0.7200000 0.0505000 0.22950000
## [3,]    0 0.0000000 0.6180982 0.38190184
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Female_80-84`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8160931 0.00000000 0.1839069
## [2,]    0 0.6313457 0.03384367 0.3348106
## [3,]    0 0.0000000 0.48024316 0.5197568
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Female_85-89`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.6559712 0.00000000 0.3440288
## [2,]    0 0.4962816 0.01883986 0.4848785
## [3,]    0 0.0000000 0.37564767 0.6243523
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## $`Female_90-94`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.4385294 0.000000000 0.5614706
## [2,]    0 0.3344867 0.005767013 0.6597463
## [3,]    0 0.0000000 0.268041237 0.7319588
## [4,]    0 0.0000000 0.000000000 1.0000000
## 
## $`Female_95et+`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.3949789 0.000000000 0.6050211
## [2,]    0 0.2912088 0.005494505 0.7032967
## [3,]    0 0.0000000 0.222222222 0.7777778
## [4,]    0 0.0000000 0.000000000 1.0000000
names(transition_matrices) <- NULL  

males <- transition_matrices[1:10]
females <- transition_matrices[11:20]

matrices_mf <- list(males, females)
matrices_mf
## [[1]]
## [[1]][[1]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9712352 0.0000000 0.02876483
## [2,]    0 0.8423077 0.1076923 0.05000000
## [3,]    0 0.0000000 0.9291339 0.07086614
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[2]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9574518 0.00000000 0.04254822
## [2,]    0 0.8469388 0.08367347 0.06938776
## [3,]    0 0.0000000 0.87280702 0.12719298
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[3]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9433756 0.0000000 0.05662437
## [2,]    0 0.8275000 0.0675000 0.10500000
## [3,]    0 0.0000000 0.8191489 0.18085106
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[4]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9224868 0.00000000 0.07751319
## [2,]    0 0.7518892 0.06801008 0.18010076
## [3,]    0 0.0000000 0.69558600 0.30441400
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[5]]
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.8875735 0.0000000 0.1124265
## [2,]    0 0.7059757 0.0560550 0.2379693
## [3,]    0 0.0000000 0.5703704 0.4296296
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## [[1]][[6]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8201575 0.00000000 0.1798425
## [2,]    0 0.6240631 0.04970414 0.3262327
## [3,]    0 0.0000000 0.48199768 0.5180023
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[7]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.7046099 0.00000000 0.2953901
## [2,]    0 0.5081301 0.03399852 0.4578714
## [3,]    0 0.0000000 0.33866995 0.6613300
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[8]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.5279737 0.00000000 0.4720263
## [2,]    0 0.3530405 0.02083333 0.6261261
## [3,]    0 0.0000000 0.25708502 0.7429150
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[9]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.3260733 0.00000000 0.6739267
## [2,]    0 0.2357595 0.01107595 0.7531646
## [3,]    0 0.0000000 0.16030534 0.8396947
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[10]]
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.2304007 0.0000000 0.7695993
## [2,]    0 0.1511628 0.0000000 0.8488372
## [3,]    0 0.0000000 0.1111111 0.8888889
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## 
## [[2]]
## [[2]][[1]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9864538 0.0000000 0.01354618
## [2,]    0 0.9042904 0.0660066 0.02970297
## [3,]    0 0.0000000 0.9193548 0.08064516
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[2]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9814785 0.00000000 0.01852146
## [2,]    0 0.9093023 0.03953488 0.05116279
## [3,]    0 0.0000000 0.86885246 0.13114754
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[3]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9750718 0.00000000 0.02492824
## [2,]    0 0.8920455 0.05965909 0.04829545
## [3,]    0 0.0000000 0.85654008 0.14345992
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[4]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9644648 0.00000000 0.03553525
## [2,]    0 0.8446281 0.04793388 0.10743802
## [3,]    0 0.0000000 0.77889447 0.22110553
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[5]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9455591 0.00000000 0.05444087
## [2,]    0 0.7926174 0.05838926 0.14899329
## [3,]    0 0.0000000 0.71125265 0.28874735
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[6]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9040836 0.0000000 0.09591642
## [2,]    0 0.7200000 0.0505000 0.22950000
## [3,]    0 0.0000000 0.6180982 0.38190184
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[7]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8160931 0.00000000 0.1839069
## [2,]    0 0.6313457 0.03384367 0.3348106
## [3,]    0 0.0000000 0.48024316 0.5197568
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[8]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.6559712 0.00000000 0.3440288
## [2,]    0 0.4962816 0.01883986 0.4848785
## [3,]    0 0.0000000 0.37564767 0.6243523
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[9]]
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.4385294 0.000000000 0.5614706
## [2,]    0 0.3344867 0.005767013 0.6597463
## [3,]    0 0.0000000 0.268041237 0.7319588
## [4,]    0 0.0000000 0.000000000 1.0000000
## 
## [[2]][[10]]
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.3949789 0.000000000 0.6050211
## [2,]    0 0.2912088 0.005494505 0.7032967
## [3,]    0 0.0000000 0.222222222 0.7777778
## [4,]    0 0.0000000 0.000000000 1.0000000
for (i in 1:length(males)) {
  colnames(males[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
  rownames(males[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
}
for (i in 1:length(females)) {
  colnames(females[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males)) {
  dimnames(males[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females)) {
  dimnames(females[[i]]) <- list(row_names_f, col_names_f)
}

transition_matrices_mf <- list(males, females)
transition_matrices_mf
## [[1]]
## [[1]][[1]]
##       P.m     MPD.m     APD.m        D.m
## P.m     0 0.9712352 0.0000000 0.02876483
## MPD.m   0 0.8423077 0.1076923 0.05000000
## APD.m   0 0.0000000 0.9291339 0.07086614
## D.m     0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[2]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9574518 0.00000000 0.04254822
## MPD.m   0 0.8469388 0.08367347 0.06938776
## APD.m   0 0.0000000 0.87280702 0.12719298
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[3]]
##       P.m     MPD.m     APD.m        D.m
## P.m     0 0.9433756 0.0000000 0.05662437
## MPD.m   0 0.8275000 0.0675000 0.10500000
## APD.m   0 0.0000000 0.8191489 0.18085106
## D.m     0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[4]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9224868 0.00000000 0.07751319
## MPD.m   0 0.7518892 0.06801008 0.18010076
## APD.m   0 0.0000000 0.69558600 0.30441400
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[5]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.8875735 0.0000000 0.1124265
## MPD.m   0 0.7059757 0.0560550 0.2379693
## APD.m   0 0.0000000 0.5703704 0.4296296
## D.m     0 0.0000000 0.0000000 1.0000000
## 
## [[1]][[6]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8201575 0.00000000 0.1798425
## MPD.m   0 0.6240631 0.04970414 0.3262327
## APD.m   0 0.0000000 0.48199768 0.5180023
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[7]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.7046099 0.00000000 0.2953901
## MPD.m   0 0.5081301 0.03399852 0.4578714
## APD.m   0 0.0000000 0.33866995 0.6613300
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[8]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.5279737 0.00000000 0.4720263
## MPD.m   0 0.3530405 0.02083333 0.6261261
## APD.m   0 0.0000000 0.25708502 0.7429150
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[9]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.3260733 0.00000000 0.6739267
## MPD.m   0 0.2357595 0.01107595 0.7531646
## APD.m   0 0.0000000 0.16030534 0.8396947
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[10]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.2304007 0.0000000 0.7695993
## MPD.m   0 0.1511628 0.0000000 0.8488372
## APD.m   0 0.0000000 0.1111111 0.8888889
## D.m     0 0.0000000 0.0000000 1.0000000
## 
## 
## [[2]]
## [[2]][[1]]
##       P.f     MPD.f     APD.f        D.f
## P.f     0 0.9864538 0.0000000 0.01354618
## MPD.f   0 0.9042904 0.0660066 0.02970297
## APD.f   0 0.0000000 0.9193548 0.08064516
## D.f     0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[2]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9814785 0.00000000 0.01852146
## MPD.f   0 0.9093023 0.03953488 0.05116279
## APD.f   0 0.0000000 0.86885246 0.13114754
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[3]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9750718 0.00000000 0.02492824
## MPD.f   0 0.8920455 0.05965909 0.04829545
## APD.f   0 0.0000000 0.85654008 0.14345992
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[4]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9644648 0.00000000 0.03553525
## MPD.f   0 0.8446281 0.04793388 0.10743802
## APD.f   0 0.0000000 0.77889447 0.22110553
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[5]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9455591 0.00000000 0.05444087
## MPD.f   0 0.7926174 0.05838926 0.14899329
## APD.f   0 0.0000000 0.71125265 0.28874735
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[6]]
##       P.f     MPD.f     APD.f        D.f
## P.f     0 0.9040836 0.0000000 0.09591642
## MPD.f   0 0.7200000 0.0505000 0.22950000
## APD.f   0 0.0000000 0.6180982 0.38190184
## D.f     0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[7]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.8160931 0.00000000 0.1839069
## MPD.f   0 0.6313457 0.03384367 0.3348106
## APD.f   0 0.0000000 0.48024316 0.5197568
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[8]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.6559712 0.00000000 0.3440288
## MPD.f   0 0.4962816 0.01883986 0.4848785
## APD.f   0 0.0000000 0.37564767 0.6243523
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[9]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.4385294 0.000000000 0.5614706
## MPD.f   0 0.3344867 0.005767013 0.6597463
## APD.f   0 0.0000000 0.268041237 0.7319588
## D.f     0 0.0000000 0.000000000 1.0000000
## 
## [[2]][[10]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.3949789 0.000000000 0.6050211
## MPD.f   0 0.2912088 0.005494505 0.7032967
## APD.f   0 0.0000000 0.222222222 0.7777778
## D.f     0 0.0000000 0.000000000 1.0000000
transition_matrices_m <- transition_matrices_mf[[1]]
transition_matrices_f <- transition_matrices_mf[[2]]

extract_rows_as_named_list <- function(matrix) {
  list(
    P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
    MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
    APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
    D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
  )
}

transition_prob_m <- lapply(transition_matrices_m, extract_rows_as_named_list)

transition_prob_f <- lapply(transition_matrices_f, extract_rows_as_named_list)

print("Transition Probabilities for Males:")
## [1] "Transition Probabilities for Males:"
print(transition_prob_m)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8423077 0.1076923 0.0500000 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.84693878 0.08367347 0.06938776 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##      P    MPD    APD      D 
## 0.0000 0.8275 0.0675 0.1050 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.75188917 0.06801008 0.18010076 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.7059757 0.0560550 0.2379693 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.62406312 0.04970414 0.32623274 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.50813008 0.03399852 0.45787140 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.35304054 0.02083333 0.62612613 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.23575949 0.01107595 0.75316456 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2304007 0.0000000 0.7695993 
## 
## [[10]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.1511628 0.0000000 0.8488372 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Transition Probabilities for Females:")
## [1] "Transition Probabilities for Females:"
print(transition_prob_f)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.90429043 0.06600660 0.02970297 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.90930233 0.03953488 0.05116279 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.89204545 0.05965909 0.04829545 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.84462810 0.04793388 0.10743802 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.79261745 0.05838926 0.14899329 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##      P    MPD    APD      D 
## 0.0000 0.7200 0.0505 0.2295 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.63134569 0.03384367 0.33481064 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.49628161 0.01883986 0.48487853 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.334486736 0.005767013 0.659746251 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3949789 0.0000000 0.6050211 
## 
## [[10]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.291208791 0.005494505 0.703296703 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
graph_prob_mf <- ggplot(final_data1, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
  theme_minimal() +
  labs(title = "Probability of death with respect to severity, baseline scenario",
       x = "Age class",
       y = "Probability",
       color = "Severity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf

Graphs showcasing the probability of remaining MPD:

extract_probabilities2 <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2]
    ))
    
  }
  
  return(data)
}

males_data_rem <- extract_probabilities2(males, age_classes, "Male")
females_data_rem <- extract_probabilities2(females, age_classes, "Female")

final_data_rem <- rbind(males_data_rem, females_data_rem)

graph_prob_mf_rem <- ggplot(final_data_rem, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD with respect to gender and age classes, baseline scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem

Graphs showcasing the probability of transitioning from MPD to APD:

extract_probabilities1 <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3]
    ))
    
  }
  
  return(data)
}

males_data_tra <- extract_probabilities1(males, age_classes, "Male")
females_data_tra <- extract_probabilities1(females, age_classes, "Female")

final_data_tra <- rbind(males_data_tra, females_data_tra)

graph_prob_mf_tra <- ggplot(final_data_tra, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, baseline scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra

Patients diagnosed with PD in 2016: costs

The database for cost calculations contains 26274 patients, due to an operation of data cleaning for a more meaningful specification of costs, and 3 additional variables:

All these costs have been extracted from the “Cartologie des Pathologies” and are referred to the 2016-2020 period, matching the length of the Markov cycle.

library(readxl)
library(tidyverse)

df2 <- read_excel("Parkinson data costs.xlsx") %>% 
  filter(first_year == "2016")%>% mutate(
    across(
      c(  "True costs"  ),
      ~ as.numeric(.x)
    )
  )

Now let’s decompose the new database into cohorts. Regarding the new variables, the weighted average of the ‘True costs’ contained in df2 shall be considered, assuming that all newly diagnosed individuals are at the mild stage at the beginning of the 5-year period and either at the mild or severe stage at the end of the period: patients who have transitioned are to be considered as severe at the end of the period.

library(dplyr)

summary_df3 <- df2 %>% 
  mutate(
    yod_binary = case_when(
      yod != "Alive" ~ "Dead",
      TRUE ~ yod
    ),
    gender = factor(BEN_SEX_COD, levels = c("1", "2"), labels = c("Male", "Female"))
  ) %>% 
  group_by(CLA_AGE_5, severity_at_end, gender) %>% 
  summarise(
    n_patients = sum(`Number of Parkinson cases`),
    Mean_reimbursed = weighted.mean(`True costs`, `Number of Parkinson cases`),
   
  ) %>% 
  arrange(gender)

summary_df3

To evaluate the relevance of the criteria used to distinguish between mild and severe PD, it is useful to calculate the differences between the average cost for a patient with severe PD and the average cost for a patient with mild PD, taking into account the decomposition into cohorts. Before examining the results, we would expect that the average cost for patients with severe PD is significantly higher than the average cost for patients with mild PD: if this is the case, then the criteria effectively distinguish between two groups of patients with very different cost profiles.

library(ggplot2)

difference_df <- summary_df3 %>%
       pivot_wider(id_cols = CLA_AGE_5, names_from = c(gender, severity_at_end), values_from = Mean_reimbursed) 
difference_df
mean_reimbursed_difference_male <- difference_df$Male_Severe - difference_df$Male_Mild
mean_reimbursed_difference_female <- difference_df$Female_Severe - difference_df$Female_Mild
 


male_plot <- ggplot(difference_df, aes(x = CLA_AGE_5, y = mean_reimbursed_difference_male)) +
  geom_bar(stat = "identity", fill = "lightblue", position = "dodge") +
  labs(title = "Difference in Mean Reimbursement Between Parkinson's Severity (Males)",
       x = "Age Classes",
       y = "Difference in Mean Reimbursement")
  theme_minimal()

female_plot <- ggplot(difference_df, aes(x = CLA_AGE_5, y = mean_reimbursed_difference_female)) +
  geom_bar(stat = "identity", fill = "pink", position = "dodge") +
  labs(title = "Difference in Mean Reimbursement Between Parkinson's Severity (Females)",
       x = "Age Classes",
       y = "Difference in Mean Reimbursement")
  theme_minimal()

print(male_plot)

print(female_plot)

The two graphs show that the differences are indeed significant and seem to follow a random trend for females and a decreasing trend for males. Such a decreasing trend would suggest a deterioration in average overall medical conditions as male patients get older, which narrows the difference between the average costs of the 2 phases of the disease. In particular, the differences among males aged 95 and over are negative, but this is due to the insignificant sample size for these very old male patients. On the other hand, all the other differences are particularly significant, leading to the conclusion that hospitalization is a valid indicator for distinguishing between the two stages of the disease.

difference_df2 <- difference_df %>% pivot_longer(cols = -CLA_AGE_5, names_to = "severity", values_to = "cost")




library(ggplot2)
costs_males <- ggplot(difference_df2 %>% filter(str_detect(severity, "Male")), aes(x = CLA_AGE_5, y = cost, group = severity, colour = severity)) +
  geom_line() +
  labs(title = "Average medical costs (males)",
       x = "Age Classes",
       y = "Cost") +
  theme_minimal()

costs_females <- ggplot(difference_df2 %>% filter(str_detect(severity, "Female")), aes(x = CLA_AGE_5, y = cost, group = severity, colour = severity)) +
  geom_line() +
  labs(title = "Average medical costs (females)",
       x = "Age Classes",
       y = "Cost") +
  theme_minimal()
options(scipen=999)
costs_males 

costs_females

The lines representing the average costs of patients with severe PD are consistently above the lines showing the average costs of patients with mild PD, once again proving the relevance of the criterion. In addition, even if a clear decreasing trend is not exhibited, the average medical cost of the oldest cohort is always lower than that of the youngest cohort. Common wisdom would suggest that average medical costs increase with age as patients are more prone to illnesses, but the above evidence shows that it is not the case for PD patients. A possible explanation is based on the characteristics of PD: medical literature suggests that the response to treatments is negatively associated with age both for levodopa administration and deep brain stimulation, hence the cost-effectiveness of treatments decreases with age. As a result, medical costs can be decomposed into a fixed, constant component, representing costs that are not associated with PD, and a variable component, which is directly related to PD. The fixed component is represented by the average medical costs of healthy patients while the variable component is the average extra cost of PD patients.

Patients diagnosed with PD in 2016: average extra costs

The vector of costs is C = (cp, c, C, 0), where cp is the average medical cost for prodromal patients, c is the average extra cost for patients affected by MPD, which is equal to the difference between the average medical cost of MPD patients and cp, C is the average extra cost associated with APD patient and deceased patients cost 0.

cp is assumed to be equal to the average medical cost of a healthy patient. The database containing healthy patients, where an healthy patient is defined as a generic patient not being affected by PD. The new variables are:

The new database shall be merged with the database indicating average medical costs of PD patients, after having applied a filter to retain patients who have been alive throughout the entire period in order to avoid the inclusion of average costs that incorporate costs of dead patients:

library(readxl)
library(tidyverse)

df3 <- read_excel("Healthy cohort.xlsx") %>% 
  filter(n_years == 5) %>% 
  mutate(
    yod_binary = case_when(
      yod != "Alive" ~ "Dead",
      TRUE ~ yod
    ),
  ) %>% 
  group_by(CLA_AGE_5, BEN_SEX_COD) %>% 
  summarise(
    Mean_reimbursed = weighted.mean(as.numeric(mean_cost), n),
    n_patients = sum(n),
    severity_at_end = "Healthy"
  ) %>% 
  mutate(gender = case_when(BEN_SEX_COD == 1 ~ "Male", BEN_SEX_COD == 2 ~ "Female")) %>% 
  select(-BEN_SEX_COD) %>% 
  bind_rows(summary_df3)
df3
difference_df3 <- df3 %>%
       pivot_wider(id_cols = CLA_AGE_5, names_from = c(gender, severity_at_end), values_from = Mean_reimbursed) 
difference_df3

The average extra costs for MPD and APD patients, c and C, will be:

difference_df4 <- difference_df3 %>% 
mutate(
    Extra_Cost_Mild_Males = Male_Mild - Male_Healthy,
    Extra_Cost_Severe_Males = Male_Severe - Male_Healthy,
    Extra_Cost_Mild_Females = Female_Mild - Female_Healthy,
    Extra_Cost_Severe_Females = Female_Severe - Female_Healthy
  ) %>%
  select(CLA_AGE_5, Extra_Cost_Mild_Males, Extra_Cost_Severe_Males,  Extra_Cost_Mild_Females , Extra_Cost_Severe_Females)

difference_df4

Some extra costs concerning the oldest patients appear to be negative, but this is because of the particular definition of healthy: a healthy patient is not affected by PD but might be affected by other medical conditions that make medical costs increase, especially at an old age. Since negative extra costs are not meaningful, let’s assume that negative deltas are equal to 0.

difference_df5 <- difference_df3 %>% 
 mutate(
    Extra_Cost_Mild_Males = ifelse(Male_Mild - Male_Healthy < 0, 0, Male_Mild - Male_Healthy),
    Extra_Cost_Severe_Males = ifelse(Male_Severe - Male_Healthy < 0, 0, Male_Severe - Male_Healthy),
    Extra_Cost_Mild_Females = ifelse(Female_Mild - Female_Healthy < 0, 0, Female_Mild - Female_Healthy),
    Extra_Cost_Severe_Females = ifelse(Female_Severe - Female_Healthy < 0, 0, Female_Severe - Female_Healthy)
  ) %>%
  select(CLA_AGE_5, Extra_Cost_Mild_Males, Extra_Cost_Severe_Males, Extra_Cost_Mild_Females, Extra_Cost_Severe_Females  )

difference_df5 

The graphs showcasing extra costs are the following:

difference_df6<- difference_df5 %>% 
  pivot_longer(cols = -CLA_AGE_5, names_to = "severity", values_to = "cost")
  
p1 <- ggplot(data = difference_df6 %>% filter(severity %in% c("Extra_Cost_Mild_Males", "Extra_Cost_Severe_Males")), aes(x = CLA_AGE_5, y = cost, group =  severity, color = severity)) +
  geom_line() +
  ggtitle("Average Extra Costs (Males)") +
  xlab("Age Class") +
  ylab("Average extra cost") +
  theme_minimal()

p2 <- ggplot(data = difference_df6 %>% filter(severity %in% c("Extra_Cost_Mild_Females", "Extra_Cost_Severe_Females")), aes(x = CLA_AGE_5, y = cost, group =  severity, color = severity)) +
  geom_line() +
  ggtitle("Average Extra Costs (Females)") +
  xlab("Age Class") +
  ylab("Average extra cost") +
  theme_minimal()

p1

p2

These graphs exhibit a similar trend compared to the ones referring to average medical costs. Again, the blue lines are consistently above the red lines and the trend is roughly decreasing, due to the decreasing cost-effectiveness of treatments as PD progresses. The fact that average medical costs are similar to the total medical costs indicates that PD is a significant driver of healthcare expenses, implying that a substantial portion of PD patients’ medical costs is directly attributable to PD itself. Moreover, the assumption that the fixed component (namely average medical costs of healthy patients) was constant is acceptable since the above graphs can be approximated by average medical costs minus a constant.

This constant is equal to the weighted average of healthy patients’ average medical costs, separately for male and female patients.

df_filtered_males <- df3 %>% 
  filter(severity_at_end == "Healthy", gender == "Male")
constant_males <- weighted.mean(df_filtered_males$Mean_reimbursed, df_filtered_males$n_patients)
df_filtered_females <- df3 %>%
  filter(severity_at_end == "Healthy", gender == "Female")
constant_females <- weighted.mean(df_filtered_females$Mean_reimbursed, df_filtered_females$n_patients)
constant_males
## [1] 21220.79
constant_females
## [1] 19613.64

Let’s subtract the constants to observe the similarity between the graphs:

library(ggplot2)
costs_males1 <- ggplot(difference_df2 %>% filter(str_detect(severity, "Male")), aes(x = CLA_AGE_5, y = cost - constant_males, group = severity, colour = severity)) +
  geom_line() +
  labs(title = "Average medical costs - constant (males)",
       x = "Age Classes",
       y = "Cost") +
  theme_minimal()

costs_females1 <- ggplot(difference_df2 %>% filter(str_detect(severity, "Female")), aes(x = CLA_AGE_5, y = cost - constant_females, group = severity, colour = severity)) +
  geom_line() +
  labs(title = "Average medical costs - constant (females)",
       x = "Age Classes",
       y = "Cost") +
  theme_minimal()
options(scipen=999)
costs_males1 

p1

costs_females1

p2

At this point the matrix of costs can be constructed:

costs_model_males <- data.frame(cp = difference_df3$Male_Healthy, c = difference_df5$Extra_Cost_Mild_Males, C = difference_df5$Extra_Cost_Severe_Males, D = 0 )
col_names_costs <- c("cp", "c", "C", "D")
rownames(costs_model_males) <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95+")
row_names_costs <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95+")
dimnames(costs_model_males) <- list(row_names_costs, col_names_costs)
costs_model_females <- data.frame(cp = difference_df3$Female_Healthy, c = difference_df5$Extra_Cost_Mild_Females, C = difference_df5$Extra_Cost_Severe_Females, D = 0)
rownames(costs_model_females) <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95+")
dimnames(costs_model_females) <- list(row_names_costs, col_names_costs)
costs_model_males
costs_model_females

Baseline scenario

Once transition matrices and cost matrices are available, the microsimulation model can be initialized:

n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women (hence, a proper approximation is: 60% men and 40% women)
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 period

Costs are to be mapped before entering microsimulation:

#Males
transition_costs_m <- list()
for (cycle in 1:10) {
  c.P.m <- costs_model_males[[cycle, "cp"]]
  c.MPD.m <- costs_model_males[[cycle, "c"]]
  c.APD.m <- costs_model_males[[cycle, "C"]]
  c.D.m <- costs_model_males[[cycle, "D"]]
  transition_costs_m[[cycle]] <- list(
    "P" = c(c.P.m),
    "MPD" = c(c.MPD.m),
    "APD" = c(c.APD.m),
    "D" = c(c.D.m)
  )
}

#When patients go beyond 95 years of age, costs for the last age class are to be repeated 
last_transition_m <- transition_costs_m[[10]]
for (i in 11:n.t) {
  transition_costs_m[[i]] <- last_transition_m
}

print(transition_costs_m)
## [[1]]
## [[1]]$P
## [1] 16244.98
## 
## [[1]]$MPD
## [1] 30039.15
## 
## [[1]]$APD
## [1] 82777.9
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 19504.67
## 
## [[2]]$MPD
## [1] 18805.09
## 
## [[2]]$APD
## [1] 52417.23
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 18095.52
## 
## [[3]]$MPD
## [1] 14841.59
## 
## [[3]]$APD
## [1] 54636.55
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 20104.62
## 
## [[4]]$MPD
## [1] 18675.96
## 
## [[4]]$APD
## [1] 46795.03
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 23982.05
## 
## [[5]]$MPD
## [1] 18764.37
## 
## [[5]]$APD
## [1] 45958.37
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 27682.73
## 
## [[6]]$MPD
## [1] 17788
## 
## [[6]]$APD
## [1] 36210.67
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 31413.43
## 
## [[7]]$MPD
## [1] 15104.06
## 
## [[7]]$APD
## [1] 33332.77
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 33994.4
## 
## [[8]]$MPD
## [1] 9020.232
## 
## [[8]]$APD
## [1] 23602.49
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 34330
## 
## [[9]]$MPD
## [1] 5341.272
## 
## [[9]]$APD
## [1] 19485.06
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 31343.84
## 
## [[10]]$MPD
## [1] 6355.477
## 
## [[10]]$APD
## [1] 0
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 31343.84
## 
## [[11]]$MPD
## [1] 6355.477
## 
## [[11]]$APD
## [1] 0
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 31343.84
## 
## [[12]]$MPD
## [1] 6355.477
## 
## [[12]]$APD
## [1] 0
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 31343.84
## 
## [[13]]$MPD
## [1] 6355.477
## 
## [[13]]$APD
## [1] 0
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 31343.84
## 
## [[14]]$MPD
## [1] 6355.477
## 
## [[14]]$APD
## [1] 0
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 31343.84
## 
## [[15]]$MPD
## [1] 6355.477
## 
## [[15]]$APD
## [1] 0
## 
## [[15]]$D
## [1] 0
#Females
transition_costs_f <- list()
for (cycle in 1:10) {
  c.P.f <- costs_model_females[[cycle, "cp"]]
  c.MPD.f <- costs_model_females[[cycle, "c"]]
  c.APD.f <- costs_model_females[[cycle, "C"]]
  c.D.f <- costs_model_females[[cycle, "D"]]
  transition_costs_f[[cycle]] <- list(
    "P" = c(c.P.f),
    "MPD" = c(c.MPD.f),
    "APD" = c(c.APD.f),
    "D" = c(c.D.f)
  )
}

#When patients go beyond 95 years of age, costs for the last age class are to be repeated 
last_transition_f <- transition_costs_f[[10]]
for (i in 11:n.t) {
  transition_costs_f[[i]] <- last_transition_f
}

print(transition_costs_f)
## [[1]]
## [[1]]$P
## [1] 15407.55
## 
## [[1]]$MPD
## [1] 24292.53
## 
## [[1]]$APD
## [1] 55993.02
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 17127.23
## 
## [[2]]$MPD
## [1] 24368.35
## 
## [[2]]$APD
## [1] 66431.63
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 15257.73
## 
## [[3]]$MPD
## [1] 16594.83
## 
## [[3]]$APD
## [1] 64962.58
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 16518.64
## 
## [[4]]$MPD
## [1] 15286.68
## 
## [[4]]$APD
## [1] 50340.51
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 20152.18
## 
## [[5]]$MPD
## [1] 21780.85
## 
## [[5]]$APD
## [1] 34621.54
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 24240.13
## 
## [[6]]$MPD
## [1] 18533.03
## 
## [[6]]$APD
## [1] 41807.45
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 29048.55
## 
## [[7]]$MPD
## [1] 19459.15
## 
## [[7]]$APD
## [1] 42848.83
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 33111.87
## 
## [[8]]$MPD
## [1] 12637.32
## 
## [[8]]$APD
## [1] 34938.64
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 34249.8
## 
## [[9]]$MPD
## [1] 2801.658
## 
## [[9]]$APD
## [1] 35427.99
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 30843.99
## 
## [[10]]$MPD
## [1] 0
## 
## [[10]]$APD
## [1] 11693.52
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 30843.99
## 
## [[11]]$MPD
## [1] 0
## 
## [[11]]$APD
## [1] 11693.52
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 30843.99
## 
## [[12]]$MPD
## [1] 0
## 
## [[12]]$APD
## [1] 11693.52
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 30843.99
## 
## [[13]]$MPD
## [1] 0
## 
## [[13]]$APD
## [1] 11693.52
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 30843.99
## 
## [[14]]$MPD
## [1] 0
## 
## [[14]]$APD
## [1] 11693.52
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 30843.99
## 
## [[15]]$MPD
## [1] 0
## 
## [[15]]$APD
## [1] 11693.52
## 
## [[15]]$D
## [1] 0

The microsimulation function for male patients is:

m.M <- m.C <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_males
#Males
Probs <- function(state){
  return(transition_prob_m[[state]])
}
Costs <- function(state) {
  return(transition_costs_m[[state]])
}

# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_m <- transition_prob_m %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_m[[10]][[current_state]]), 1, prob = transition_prob_m[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_m[[t]][[current_state]]), 1, prob = transition_prob_m[[t]][[current_state]])
         }
        m.M[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M)
}


# Init m.M #repeat it!!!!
model_results_m <- list()
for(i in 1:n.sim) {
m.M <- m.C <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_males
# Microsim loop
model_results_m[[i]] <- loop_microsim(n.t)
print(i)
} 
## [1] 1
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## [1] 100
# repeat it!!!

#Results of the median cycle, the 50th
model_results_m[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 2   "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 3   "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 5   "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 7   "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 8   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 9   "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 11  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 15  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 17  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 18  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 19  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 22  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 26  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 30  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 31  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 32  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 33  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 38  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 43  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 48  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 53  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 55  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 60  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 61  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 64  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 65  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 66  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 70  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 71  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 72  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 74  "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 76  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 80  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 81  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 82  "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 85  "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 87  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 90  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 92  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 94  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 96  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 97  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 98  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 101 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 102 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 104 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 105 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 107 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 108 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 112 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 116 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 117 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 120 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 124 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 125 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 128 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 129 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 132 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 133 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 135 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 137 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 139 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 141 "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 143 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 145 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 152 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 155 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 156 "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 164 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 165 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 166 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 168 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 173 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 174 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 178 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 179 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 182 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 188 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 190 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 196 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 197 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 198 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 203 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 204 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 206 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 207 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 208 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 209 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 211 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 213 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 214 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 217 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 220 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 224 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 228 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 230 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 233 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 235 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 238 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 239 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 241 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 243 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 244 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 250 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 255 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 258 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 259 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 267 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 268 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 269 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 270 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 272 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 273 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 278 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 279 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 281 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 287 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 294 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 296 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 299 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 2   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 20  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 21  "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 22  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 23  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 24  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 25  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 26  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 27  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 28  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 29  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 30  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 31  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 32  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 33  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 34  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 35  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 36  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 37  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 38  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 39  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 40  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 41  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 42  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 43  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 44  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 45  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 46  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 47  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 48  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 49  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 50  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 51  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 52  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 53  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 54  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 55  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 56  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 57  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 58  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 59  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 60  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 61  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 62  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 63  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 64  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 65  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 66  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 67  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 68  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 69  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 70  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 71  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 72  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 73  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 74  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 75  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 76  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 77  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 78  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 79  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 80  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 81  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 82  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 83  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 84  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 85  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 86  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 87  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 88  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 89  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 90  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 91  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 92  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 93  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 94  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 95  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 96  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 97  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 98  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 99  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 100 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 101 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 102 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 103 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 104 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 105 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 106 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 107 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 108 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 109 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 110 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 111 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 112 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 113 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 114 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 115 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 116 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 117 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 118 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 119 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 120 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 121 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 122 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 123 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 124 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 125 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 126 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 127 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 128 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 129 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 130 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 131 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 132 "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 133 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 134 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 284 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 285 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 298 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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df_m.M <- model_results_m[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 15600       1
## 
## [[2]]
##  cycle 1     n    percent
##        D   475 0.03044872
##      MPD 15125 0.96955128
## 
## [[3]]
##  cycle 2     n    percent
##      APD  1194 0.07653846
##        D  1492 0.09564103
##      MPD 12914 0.82782051
## 
## [[4]]
##  cycle 3     n   percent
##      APD  1878 0.1203846
##        D  3082 0.1975641
##      MPD 10640 0.6820513
## 
## [[5]]
##  cycle 4    n   percent
##      APD 1997 0.1280128
##        D 5622 0.3603846
##      MPD 7981 0.5116026
## 
## [[6]]
##  cycle 5    n   percent
##      APD 1602 0.1026923
##        D 8412 0.5392308
##      MPD 5586 0.3580769
## 
## [[7]]
##  cycle 6     n    percent
##      APD  1059 0.06788462
##        D 11097 0.71134615
##      MPD  3444 0.22076923
## 
## [[8]]
##  cycle 7     n    percent
##      APD   453 0.02903846
##        D 13390 0.85833333
##      MPD  1757 0.11262821
## 
## [[9]]
##  cycle 8     n     percent
##      APD   138 0.008846154
##        D 14810 0.949358974
##      MPD   652 0.041794872
## 
## [[10]]
##  cycle 9     n     percent
##      APD    31 0.001987179
##        D 15436 0.989487179
##      MPD   133 0.008525641
## 
## [[11]]
##  cycle 10     n      percent
##       APD     5 0.0003205128
##         D 15575 0.9983974359
##       MPD    20 0.0012820513
## 
## [[12]]
##  cycle 11     n       percent
##       APD     3 0.00019230769
##         D 15596 0.99974358974
##       MPD     1 0.00006410256
## 
## [[13]]
##  cycle 12     n percent
##         D 15600       1
## 
## [[14]]
##  cycle 13     n percent
##         D 15600       1
## 
## [[15]]
##  cycle 14     n percent
##         D 15600       1
# Transition costs in a dataframe
transition_costs_m <-
  transition_costs_m %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
    "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")

final_cost_m <-
  map(
    model_results_m,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_m
      )
  )
  

final_cost_m2 <-
  map(
    final_cost_m,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
    filter(cycle != "cycle 15")
  )
final_cost_m2
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 257921436.
##  4 cycle 3  15600 286047672.
##  5 cycle 4  15600 243938519.
##  6 cycle 5  15600 157723948.
##  7 cycle 6  15600  88021454.
##  8 cycle 7  15600  26262200.
##  9 cycle 8  15600   6358778.
## 10 cycle 9  15600    896122.
## 11 cycle 10 15600    114399.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 259540309.
##  4 cycle 3  15600 286573543.
##  5 cycle 4  15600 243666579.
##  6 cycle 5  15600 157197941.
##  7 cycle 6  15600  87577196.
##  8 cycle 7  15600  27965353.
##  9 cycle 8  15600   6807233.
## 10 cycle 9  15600    832567.
## 11 cycle 10 15600    101688.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 260143267.
##  4 cycle 3  15600 286947693.
##  5 cycle 4  15600 243909993.
##  6 cycle 5  15600 157229057.
##  7 cycle 6  15600  86904272.
##  8 cycle 7  15600  26347445.
##  9 cycle 8  15600   6283701.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285122707.
##  3 cycle 2  15600 262160090.
##  4 cycle 3  15600 287604716.
##  5 cycle 4  15600 244159881.
##  6 cycle 5  15600 158324280.
##  7 cycle 6  15600  88653210.
##  8 cycle 7  15600  27680164.
##  9 cycle 8  15600   6714850.
## 10 cycle 9  15600    997810.
## 11 cycle 10 15600    190664.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 260709857.
##  4 cycle 3  15600 289692867.
##  5 cycle 4  15600 243121317.
##  6 cycle 5  15600 158418932.
##  7 cycle 6  15600  88484459.
##  8 cycle 7  15600  28106507.
##  9 cycle 8  15600   6568756.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285085097.
##  3 cycle 2  15600 258097578.
##  4 cycle 3  15600 287881280.
##  5 cycle 4  15600 244252607.
##  6 cycle 5  15600 159232720.
##  7 cycle 6  15600  89529236.
##  8 cycle 7  15600  27157451.
##  9 cycle 8  15600   6278957.
## 10 cycle 9  15600    972388.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285799690.
##  3 cycle 2  15600 259101912.
##  4 cycle 3  15600 287667874.
##  5 cycle 4  15600 243218426.
##  6 cycle 5  15600 159671708.
##  7 cycle 6  15600  90789122.
##  8 cycle 7  15600  29141730.
##  9 cycle 8  15600   6808814.
## 10 cycle 9  15600    832567.
## 11 cycle 10 15600    108043.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 261139610.
##  4 cycle 3  15600 287070243.
##  5 cycle 4  15600 242985347.
##  6 cycle 5  15600 159058013.
##  7 cycle 6  15600  89027158.
##  8 cycle 7  15600  27780741.
##  9 cycle 8  15600   6791209.
## 10 cycle 9  15600   1093142.
## 11 cycle 10 15600     95332.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 260799558.
##  4 cycle 3  15600 288036786.
##  5 cycle 4  15600 242953533.
##  6 cycle 5  15600 159965184.
##  7 cycle 6  15600  88700598.
##  8 cycle 7  15600  27852758.
##  9 cycle 8  15600   6429795.
## 10 cycle 9  15600    927900.
## 11 cycle 10 15600    108043.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285574029.
##  3 cycle 2  15600 260842779.
##  4 cycle 3  15600 287119982.
##  5 cycle 4  15600 244534595.
##  6 cycle 5  15600 160287907.
##  7 cycle 6  15600  89256324.
##  8 cycle 7  15600  28380284.
##  9 cycle 8  15600   6265112.
## 10 cycle 9  15600    870700.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 260343547.
##  4 cycle 3  15600 289189668.
##  5 cycle 4  15600 244848684.
##  6 cycle 5  15600 159203474.
##  7 cycle 6  15600  88744343.
##  8 cycle 7  15600  27587542.
##  9 cycle 8  15600   6744121.
## 10 cycle 9  15600    927900.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 261062629.
##  4 cycle 3  15600 289962722.
##  5 cycle 4  15600 245341750.
##  6 cycle 5  15600 160481036.
##  7 cycle 6  15600  89017265.
##  8 cycle 7  15600  27544860.
##  9 cycle 8  15600   6147306.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    177953.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284314088.
##  3 cycle 2  15600 258894617.
##  4 cycle 3  15600 287073397.
##  5 cycle 4  15600 244955032.
##  6 cycle 5  15600 158256936.
##  7 cycle 6  15600  88704781.
##  8 cycle 7  15600  27451805.
##  9 cycle 8  15600   6504274.
## 10 cycle 9  15600    889767.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285461198.
##  3 cycle 2  15600 258113072.
##  4 cycle 3  15600 287826514.
##  5 cycle 4  15600 241297414.
##  6 cycle 5  15600 157227805.
##  7 cycle 6  15600  87860520.
##  8 cycle 7  15600  26016405.
##  9 cycle 8  15600   6382708.
## 10 cycle 9  15600   1055009.
## 11 cycle 10 15600    190664.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 260912745.
##  4 cycle 3  15600 288608190.
##  5 cycle 4  15600 246362595.
##  6 cycle 5  15600 158411933.
##  7 cycle 6  15600  88769869.
##  8 cycle 7  15600  27084684.
##  9 cycle 8  15600   6258576.
## 10 cycle 9  15600    966032.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 260298370.
##  4 cycle 3  15600 286938460.
##  5 cycle 4  15600 242360694.
##  6 cycle 5  15600 157061984.
##  7 cycle 6  15600  86630842.
##  8 cycle 7  15600  26883991.
##  9 cycle 8  15600   6647891.
## 10 cycle 9  15600    972388.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 260352354.
##  4 cycle 3  15600 286623492.
##  5 cycle 4  15600 243667388.
##  6 cycle 5  15600 158641891.
##  7 cycle 6  15600  87004271.
##  8 cycle 7  15600  27576129.
##  9 cycle 8  15600   6347199.
## 10 cycle 9  15600    838923.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 257738607.
##  4 cycle 3  15600 285789573.
##  5 cycle 4  15600 242070276.
##  6 cycle 5  15600 158872517.
##  7 cycle 6  15600  88513629.
##  8 cycle 7  15600  27861779.
##  9 cycle 8  15600   6924056.
## 10 cycle 9  15600   1016876.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 256100979.
##  4 cycle 3  15600 284914307.
##  5 cycle 4  15600 242489903.
##  6 cycle 5  15600 157170616.
##  7 cycle 6  15600  87648025.
##  8 cycle 7  15600  27724516.
##  9 cycle 8  15600   6308826.
## 10 cycle 9  15600    870700.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284803020.
##  3 cycle 2  15600 259605709.
##  4 cycle 3  15600 288540616.
##  5 cycle 4  15600 244248797.
##  6 cycle 5  15600 157684563.
##  7 cycle 6  15600  87454278.
##  8 cycle 7  15600  27787052.
##  9 cycle 8  15600   7019091.
## 10 cycle 9  15600    857989.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284276478.
##  3 cycle 2  15600 261727563.
##  4 cycle 3  15600 285464321.
##  5 cycle 4  15600 243103362.
##  6 cycle 5  15600 158021232.
##  7 cycle 6  15600  88528724.
##  8 cycle 7  15600  26980649.
##  9 cycle 8  15600   6365401.
## 10 cycle 9  15600    838923.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 261932736.
##  4 cycle 3  15600 290161659.
##  5 cycle 4  15600 246057223.
##  6 cycle 5  15600 161698236.
##  7 cycle 6  15600  89312058.
##  8 cycle 7  15600  27452410.
##  9 cycle 8  15600   6276480.
## 10 cycle 9  15600    966032.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 259421576.
##  4 cycle 3  15600 286658951.
##  5 cycle 4  15600 245219115.
##  6 cycle 5  15600 159718073.
##  7 cycle 6  15600  89505286.
##  8 cycle 7  15600  27630395.
##  9 cycle 8  15600   6408132.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    120754.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284746605.
##  3 cycle 2  15600 259209552.
##  4 cycle 3  15600 285800488.
##  5 cycle 4  15600 242072704.
##  6 cycle 5  15600 158840132.
##  7 cycle 6  15600  87166781.
##  8 cycle 7  15600  26838890.
##  9 cycle 8  15600   6216742.
## 10 cycle 9  15600    997810.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 258550652.
##  4 cycle 3  15600 284973699.
##  5 cycle 4  15600 240510406.
##  6 cycle 5  15600 156914602.
##  7 cycle 6  15600  87968845.
##  8 cycle 7  15600  27810655.
##  9 cycle 8  15600   6462827.
## 10 cycle 9  15600    864345.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 263293268.
##  4 cycle 3  15600 291714286.
##  5 cycle 4  15600 248007048.
##  6 cycle 5  15600 161356456.
##  7 cycle 6  15600  91291719.
##  8 cycle 7  15600  28157026.
##  9 cycle 8  15600   6760444.
## 10 cycle 9  15600   1010521.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 259827516.
##  4 cycle 3  15600 288119881.
##  5 cycle 4  15600 242224437.
##  6 cycle 5  15600 158283609.
##  7 cycle 6  15600  86342307.
##  8 cycle 7  15600  26587073.
##  9 cycle 8  15600   6426035.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284878241.
##  3 cycle 2  15600 258785509.
##  4 cycle 3  15600 287786237.
##  5 cycle 4  15600 241491009.
##  6 cycle 5  15600 157830658.
##  7 cycle 6  15600  85801685.
##  8 cycle 7  15600  26835431.
##  9 cycle 8  15600   6305066.
## 10 cycle 9  15600    794435.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 259328613.
##  4 cycle 3  15600 287427189.
##  5 cycle 4  15600 242444181.
##  6 cycle 5  15600 160692588.
##  7 cycle 6  15600  90043293.
##  8 cycle 7  15600  29553492.
##  9 cycle 8  15600   6820393.
## 10 cycle 9  15600    978743.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 259154263.
##  4 cycle 3  15600 286783604.
##  5 cycle 4  15600 242294302.
##  6 cycle 5  15600 154774948.
##  7 cycle 6  15600  85525121.
##  8 cycle 7  15600  26434334.
##  9 cycle 8  15600   6219606.
## 10 cycle 9  15600    889767.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 258040168.
##  4 cycle 3  15600 288880148.
##  5 cycle 4  15600 245533153.
##  6 cycle 5  15600 157334499.
##  7 cycle 6  15600  86707920.
##  8 cycle 7  15600  27086039.
##  9 cycle 8  15600   6192601.
## 10 cycle 9  15600    946966.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 258890052.
##  4 cycle 3  15600 288137086.
##  5 cycle 4  15600 245288458.
##  6 cycle 5  15600 159738400.
##  7 cycle 6  15600  89037580.
##  8 cycle 7  15600  27756533.
##  9 cycle 8  15600   6390526.
## 10 cycle 9  15600   1074076.
## 11 cycle 10 15600    184309.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 262167429.
##  4 cycle 3  15600 289300041.
##  5 cycle 4  15600 245192157.
##  6 cycle 5  15600 159713596.
##  7 cycle 6  15600  88420390.
##  8 cycle 7  15600  27458866.
##  9 cycle 8  15600   6721474.
## 10 cycle 9  15600    857989.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 261044362.
##  4 cycle 3  15600 288981709.
##  5 cycle 4  15600 244050009.
##  6 cycle 5  15600 157082945.
##  7 cycle 6  15600  86880833.
##  8 cycle 7  15600  26775748.
##  9 cycle 8  15600   6662035.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 260200025.
##  4 cycle 3  15600 288544592.
##  5 cycle 4  15600 247433833.
##  6 cycle 5  15600 161425704.
##  7 cycle 6  15600  88584467.
##  8 cycle 7  15600  28210398.
##  9 cycle 8  15600   7145700.
## 10 cycle 9  15600   1042298.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 261052517.
##  4 cycle 3  15600 289768202.
##  5 cycle 4  15600 246971198.
##  6 cycle 5  15600 158025040.
##  7 cycle 6  15600  87958942.
##  8 cycle 7  15600  27283274.
##  9 cycle 8  15600   6790611.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    114399.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 257209039.
##  4 cycle 3  15600 284748116.
##  5 cycle 4  15600 242282113.
##  6 cycle 5  15600 157155366.
##  7 cycle 6  15600  86694392.
##  8 cycle 7  15600  26848659.
##  9 cycle 8  15600   6368265.
## 10 cycle 9  15600    959677.
## 11 cycle 10 15600    171598.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 260620807.
##  4 cycle 3  15600 288942044.
##  5 cycle 4  15600 245700178.
##  6 cycle 5  15600 158167996.
##  7 cycle 6  15600  89434439.
##  8 cycle 7  15600  27835612.
##  9 cycle 8  15600   6458382.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285555224.
##  3 cycle 2  15600 259085766.
##  4 cycle 3  15600 291223665.
##  5 cycle 4  15600 246027315.
##  6 cycle 5  15600 159026863.
##  7 cycle 6  15600  87920917.
##  8 cycle 7  15600  26123294.
##  9 cycle 8  15600   5929808.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284238868.
##  3 cycle 2  15600 258097414.
##  4 cycle 3  15600 286500942.
##  5 cycle 4  15600 243838982.
##  6 cycle 5  15600 157210617.
##  7 cycle 6  15600  87055832.
##  8 cycle 7  15600  26584219.
##  9 cycle 8  15600   6168372.
## 10 cycle 9  15600    934255.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 262441264.
##  4 cycle 3  15600 291174997.
##  5 cycle 4  15600 247306765.
##  6 cycle 5  15600 160528019.
##  7 cycle 6  15600  87096972.
##  8 cycle 7  15600  27086932.
##  9 cycle 8  15600   6358180.
## 10 cycle 9  15600    915189.
## 11 cycle 10 15600    177953.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 259389120.
##  4 cycle 3  15600 285598818.
##  5 cycle 4  15600 243251858.
##  6 cycle 5  15600 158058077.
##  7 cycle 6  15600  86705843.
##  8 cycle 7  15600  26281595.
##  9 cycle 8  15600   6679341.
## 10 cycle 9  15600    966032.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 258585882.
##  4 cycle 3  15600 287435160.
##  5 cycle 4  15600 246927718.
##  6 cycle 5  15600 160181830.
##  7 cycle 6  15600  90080279.
##  8 cycle 7  15600  28029849.
##  9 cycle 8  15600   6861928.
## 10 cycle 9  15600    966032.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 259348020.
##  4 cycle 3  15600 287819804.
##  5 cycle 4  15600 244525070.
##  6 cycle 5  15600 157739163.
##  7 cycle 6  15600  86713131.
##  8 cycle 7  15600  26158020.
##  9 cycle 8  15600   6277376.
## 10 cycle 9  15600    851634.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 260444665.
##  4 cycle 3  15600 286991773.
##  5 cycle 4  15600 241073809.
##  6 cycle 5  15600 157183910.
##  7 cycle 6  15600  87008434.
##  8 cycle 7  15600  26795432.
##  9 cycle 8  15600   6535724.
## 10 cycle 9  15600   1112208.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 262085066.
##  4 cycle 3  15600 287411246.
##  5 cycle 4  15600 243351918.
##  6 cycle 5  15600 157589243.
##  7 cycle 6  15600  87174588.
##  8 cycle 7  15600  28310515.
##  9 cycle 8  15600   6979137.
## 10 cycle 9  15600   1086787.
## 11 cycle 10 15600    190664.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 257721644.
##  4 cycle 3  15600 285998163.
##  5 cycle 4  15600 242783558.
##  6 cycle 5  15600 159711709.
##  7 cycle 6  15600  88572988.
##  8 cycle 7  15600  27309729.
##  9 cycle 8  15600   6643533.
## 10 cycle 9  15600    934255.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 260032690.
##  4 cycle 3  15600 286894609.
##  5 cycle 4  15600 241489677.
##  6 cycle 5  15600 156449593.
##  7 cycle 6  15600  87685002.
##  8 cycle 7  15600  27408058.
##  9 cycle 8  15600   6443342.
## 10 cycle 9  15600    788079.
## 11 cycle 10 15600    114399.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 258499441.
##  4 cycle 3  15600 288167518.
##  5 cycle 4  15600 241933733.
##  6 cycle 5  15600 156901223.
##  7 cycle 6  15600  86822493.
##  8 cycle 7  15600  27313043.
##  9 cycle 8  15600   6499917.
## 10 cycle 9  15600    985099.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 256900304.
##  4 cycle 3  15600 286593270.
##  5 cycle 4  15600 241537253.
##  6 cycle 5  15600 157373248.
##  7 cycle 6  15600  87317803.
##  8 cycle 7  15600  26540473.
##  9 cycle 8  15600   6171447.
## 10 cycle 9  15600    845278.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285216732.
##  3 cycle 2  15600 259841870.
##  4 cycle 3  15600 287864497.
##  5 cycle 4  15600 244050296.
##  6 cycle 5  15600 159972817.
##  7 cycle 6  15600  87941761.
##  8 cycle 7  15600  28137026.
##  9 cycle 8  15600   6537604.
## 10 cycle 9  15600    991454.
## 11 cycle 10 15600    177953.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 261232573.
##  4 cycle 3  15600 288082740.
##  5 cycle 4  15600 244283325.
##  6 cycle 5  15600 158028865.
##  7 cycle 6  15600  88744353.
##  8 cycle 7  15600  27071456.
##  9 cycle 8  15600   6980718.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 260231665.
##  4 cycle 3  15600 288052939.
##  5 cycle 4  15600 246310972.
##  6 cycle 5  15600 160289193.
##  7 cycle 6  15600  88780811.
##  8 cycle 7  15600  27605004.
##  9 cycle 8  15600   6196572.
## 10 cycle 9  15600    991454.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284897046.
##  3 cycle 2  15600 261161791.
##  4 cycle 3  15600 286478901.
##  5 cycle 4  15600 245398043.
##  6 cycle 5  15600 158437354.
##  7 cycle 6  15600  87277192.
##  8 cycle 7  15600  27673852.
##  9 cycle 8  15600   6175892.
## 10 cycle 9  15600    902478.
## 11 cycle 10 15600    177953.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 261124604.
##  4 cycle 3  15600 287530643.
##  5 cycle 4  15600 244354859.
##  6 cycle 5  15600 159627212.
##  7 cycle 6  15600  90504222.
##  8 cycle 7  15600  28626972.
##  9 cycle 8  15600   6840862.
## 10 cycle 9  15600    902478.
## 11 cycle 10 15600    114399.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285592834.
##  3 cycle 2  15600 258392289.
##  4 cycle 3  15600 286607320.
##  5 cycle 4  15600 243056358.
##  6 cycle 5  15600 159717456.
##  7 cycle 6  15600  90650061.
##  8 cycle 7  15600  28354261.
##  9 cycle 8  15600   6703184.
## 10 cycle 9  15600    921544.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 258274861.
##  4 cycle 3  15600 285938560.
##  5 cycle 4  15600 241376517.
##  6 cycle 5  15600 159810153.
##  7 cycle 6  15600  89015697.
##  8 cycle 7  15600  27250335.
##  9 cycle 8  15600   6353225.
## 10 cycle 9  15600    908833.
## 11 cycle 10 15600    127110.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285310758.
##  3 cycle 2  15600 260833319.
##  4 cycle 3  15600 286222274.
##  5 cycle 4  15600 244241699.
##  6 cycle 5  15600 160611899.
##  7 cycle 6  15600  89368821.
##  8 cycle 7  15600  27092206.
##  9 cycle 8  15600   6548586.
## 10 cycle 9  15600   1029587.
## 11 cycle 10 15600    101688.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 259861277.
##  4 cycle 3  15600 287411246.
##  5 cycle 4  15600 241023518.
##  6 cycle 5  15600 157286830.
##  7 cycle 6  15600  86734993.
##  8 cycle 7  15600  26540617.
##  9 cycle 8  15600   6761639.
## 10 cycle 9  15600    946966.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 261339725.
##  4 cycle 3  15600 287298770.
##  5 cycle 4  15600 240921266.
##  6 cycle 5  15600 154487784.
##  7 cycle 6  15600  85148557.
##  8 cycle 7  15600  26887738.
##  9 cycle 8  15600   6707928.
## 10 cycle 9  15600   1023232.
## 11 cycle 10 15600    197020.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 260666801.
##  4 cycle 3  15600 289230996.
##  5 cycle 4  15600 247044401.
##  6 cycle 5  15600 158939226.
##  7 cycle 6  15600  89309453.
##  8 cycle 7  15600  27413015.
##  9 cycle 8  15600   6945807.
## 10 cycle 9  15600    991454.
## 11 cycle 10 15600    114399.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 260023882.
##  4 cycle 3  15600 288895459.
##  5 cycle 4  15600 244602319.
##  6 cycle 5  15600 158082847.
##  7 cycle 6  15600  88012590.
##  8 cycle 7  15600  27343707.
##  9 cycle 8  15600   6864792.
## 10 cycle 9  15600   1016876.
## 11 cycle 10 15600    108043.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 259210368.
##  4 cycle 3  15600 288698626.
##  5 cycle 4  15600 244320044.
##  6 cycle 5  15600 155722806.
##  7 cycle 6  15600  86737089.
##  8 cycle 7  15600  27560797.
##  9 cycle 8  15600   6444923.
## 10 cycle 9  15600    864345.
## 11 cycle 10 15600    108043.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 259334483.
##  4 cycle 3  15600 287886117.
##  5 cycle 4  15600 242522239.
##  6 cycle 5  15600 155938149.
##  7 cycle 6  15600  86190738.
##  8 cycle 7  15600  26235744.
##  9 cycle 8  15600   6083808.
## 10 cycle 9  15600    908833.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 258539400.
##  4 cycle 3  15600 285309656.
##  5 cycle 4  15600 247771305.
##  6 cycle 5  15600 161473339.
##  7 cycle 6  15600  90425058.
##  8 cycle 7  15600  28240312.
##  9 cycle 8  15600   6613963.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 258166240.
##  4 cycle 3  15600 287042124.
##  5 cycle 4  15600 241204401.
##  6 cycle 5  15600 159026262.
##  7 cycle 6  15600  87848022.
##  8 cycle 7  15600  28290226.
##  9 cycle 8  15600   6524058.
## 10 cycle 9  15600    902478.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 258719456.
##  4 cycle 3  15600 288307062.
##  5 cycle 4  15600 245606642.
##  6 cycle 5  15600 161092826.
##  7 cycle 6  15600  88263109.
##  8 cycle 7  15600  27019899.
##  9 cycle 8  15600   6439494.
## 10 cycle 9  15600   1035943.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 259756734.
##  4 cycle 3  15600 287296667.
##  5 cycle 4  15600 246597580.
##  6 cycle 5  15600 158991304.
##  7 cycle 6  15600  88935495.
##  8 cycle 7  15600  27085894.
##  9 cycle 8  15600   6528503.
## 10 cycle 9  15600    870700.
## 11 cycle 10 15600     95332.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 257495595.
##  4 cycle 3  15600 283864477.
##  5 cycle 4  15600 242301686.
##  6 cycle 5  15600 156912681.
##  7 cycle 6  15600  86460534.
##  8 cycle 7  15600  26886095.
##  9 cycle 8  15600   5799438.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284972266.
##  3 cycle 2  15600 261921320.
##  4 cycle 3  15600 289726434.
##  5 cycle 4  15600 243861843.
##  6 cycle 5  15600 159811473.
##  7 cycle 6  15600  87679271.
##  8 cycle 7  15600  27655812.
##  9 cycle 8  15600   6512778.
## 10 cycle 9  15600    959677.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 260910788.
##  4 cycle 3  15600 285199703.
##  5 cycle 4  15600 239053310.
##  6 cycle 5  15600 156260254.
##  7 cycle 6  15600  87523010.
##  8 cycle 7  15600  26652779.
##  9 cycle 8  15600   6364717.
## 10 cycle 9  15600   1029587.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 260789282.
##  4 cycle 3  15600 289444632.
##  5 cycle 4  15600 245612830.
##  6 cycle 5  15600 160226258.
##  7 cycle 6  15600  87679771.
##  8 cycle 7  15600  27275608.
##  9 cycle 8  15600   6411294.
## 10 cycle 9  15600    972388.
## 11 cycle 10 15600    184309.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 259872206.
##  4 cycle 3  15600 289316194.
##  5 cycle 4  15600 245951448.
##  6 cycle 5  15600 158108285.
##  7 cycle 6  15600  90063608.
##  8 cycle 7  15600  27412121.
##  9 cycle 8  15600   6431675.
## 10 cycle 9  15600    921544.
## 11 cycle 10 15600    177953.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 258359997.
##  4 cycle 3  15600 285937088.
##  5 cycle 4  15600 243731015.
##  6 cycle 5  15600 157396765.
##  7 cycle 6  15600  88917785.
##  8 cycle 7  15600  27472699.
##  9 cycle 8  15600   6515256.
## 10 cycle 9  15600    915189.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285179122.
##  3 cycle 2  15600 261192778.
##  4 cycle 3  15600 288138768.
##  5 cycle 4  15600 243338869.
##  6 cycle 5  15600 158404952.
##  7 cycle 6  15600  89098515.
##  8 cycle 7  15600  26546495.
##  9 cycle 8  15600   6586274.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    146176.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 259537371.
##  4 cycle 3  15600 285185443.
##  5 cycle 4  15600 238088184.
##  6 cycle 5  15600 156859334.
##  7 cycle 6  15600  87603232.
##  8 cycle 7  15600  27896505.
##  9 cycle 8  15600   6421889.
## 10 cycle 9  15600   1137630.
## 11 cycle 10 15600    184309.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 262367708.
##  4 cycle 3  15600 290060938.
##  5 cycle 4  15600 242964391.
##  6 cycle 5  15600 158014234.
##  7 cycle 6  15600  88388624.
##  8 cycle 7  15600  27255319.
##  9 cycle 8  15600   6004884.
## 10 cycle 9  15600    966032.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285141512.
##  3 cycle 2  15600 260390192.
##  4 cycle 3  15600 288902800.
##  5 cycle 4  15600 245251738.
##  6 cycle 5  15600 160133544.
##  7 cycle 6  15600  89173516.
##  8 cycle 7  15600  27498260.
##  9 cycle 8  15600   6755015.
## 10 cycle 9  15600   1042298.
## 11 cycle 10 15600    222442.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 259082828.
##  4 cycle 3  15600 286812354.
##  5 cycle 4  15600 243278765.
##  6 cycle 5  15600 160268849.
##  7 cycle 6  15600  89494345.
##  8 cycle 7  15600  28056449.
##  9 cycle 8  15600   6489832.
## 10 cycle 9  15600    883411.
## 11 cycle 10 15600    165242.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 283449054.
##  3 cycle 2  15600 255295456.
##  4 cycle 3  15600 284891024.
##  5 cycle 4  15600 240454113.
##  6 cycle 5  15600 157041657.
##  7 cycle 6  15600  88366761.
##  8 cycle 7  15600  27354686.
##  9 cycle 8  15600   6682504.
## 10 cycle 9  15600    832567.
## 11 cycle 10 15600    165242.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 258445457.
##  4 cycle 3  15600 286427499.
##  5 cycle 4  15600 242215198.
##  6 cycle 5  15600 157331325.
##  7 cycle 6  15600  86856874.
##  8 cycle 7  15600  27044685.
##  9 cycle 8  15600   6017833.
## 10 cycle 9  15600    921544.
## 11 cycle 10 15600    120754.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 261390449.
##  4 cycle 3  15600 289570317.
##  5 cycle 4  15600 247736204.
##  6 cycle 5  15600 160616976.
##  7 cycle 6  15600  89055808.
##  8 cycle 7  15600  26959611.
##  9 cycle 8  15600   6061459.
## 10 cycle 9  15600   1004165.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284332893.
##  3 cycle 2  15600 258850093.
##  4 cycle 3  15600 287373894.
##  5 cycle 4  15600 242153999.
##  6 cycle 5  15600 156717648.
##  7 cycle 6  15600  88241756.
##  8 cycle 7  15600  27392726.
##  9 cycle 8  15600   6325746.
## 10 cycle 9  15600    883411.
## 11 cycle 10 15600     88977.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285385978.
##  3 cycle 2  15600 261409368.
##  4 cycle 3  15600 288642388.
##  5 cycle 4  15600 245369180.
##  6 cycle 5  15600 158862328.
##  7 cycle 6  15600  89311001.
##  8 cycle 7  15600  27383995.
##  9 cycle 8  15600   6412576.
## 10 cycle 9  15600    807146.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 258577074.
##  4 cycle 3  15600 287416274.
##  5 cycle 4  15600 244568887.
##  6 cycle 5  15600 157950749.
##  7 cycle 6  15600  87112086.
##  8 cycle 7  15600  26802059.
##  9 cycle 8  15600   6711003.
## 10 cycle 9  15600    953322.
## 11 cycle 10 15600    203375.
## 12 cycle 11 15600         0 
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 259891613.
##  4 cycle 3  15600 286727156.
##  5 cycle 4  15600 243591522.
##  6 cycle 5  15600 159179991.
##  7 cycle 6  15600  88852687.
##  8 cycle 7  15600  26887593.
##  9 cycle 8  15600   6829880.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 260211929.
##  4 cycle 3  15600 287449650.
##  5 cycle 4  15600 242762315.
##  6 cycle 5  15600 157859854.
##  7 cycle 6  15600  87904256.
##  8 cycle 7  15600  27903999.
##  9 cycle 8  15600   6651054.
## 10 cycle 9  15600   1035943.
## 11 cycle 10 15600    228797.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 261142219.
##  4 cycle 3  15600 287350593.
##  5 cycle 4  15600 244277896.
##  6 cycle 5  15600 158136845.
##  7 cycle 6  15600  86533439.
##  8 cycle 7  15600  26437042.
##  9 cycle 8  15600   5989844.
## 10 cycle 9  15600    832567.
## 11 cycle 10 15600    120754.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285028682.
##  3 cycle 2  15600 259299582.
##  4 cycle 3  15600 287948854.
##  5 cycle 4  15600 243091695.
##  6 cycle 5  15600 158464663.
##  7 cycle 6  15600  87737073.
##  8 cycle 7  15600  27399182.
##  9 cycle 8  15600   5966001.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 257758995.
##  4 cycle 3  15600 285270202.
##  5 cycle 4  15600 243313630.
##  6 cycle 5  15600 159933450.
##  7 cycle 6  15600  91480265.
##  8 cycle 7  15600  28304664.
##  9 cycle 8  15600   6698141.
## 10 cycle 9  15600    940611.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 256890027.
##  4 cycle 3  15600 284073679.
##  5 cycle 4  15600 243637767.
##  6 cycle 5  15600 159391559.
##  7 cycle 6  15600  87838129.
##  8 cycle 7  15600  27006211.
##  9 cycle 8  15600   6274600.
## 10 cycle 9  15600    826212.
## 11 cycle 10 15600    139820.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 259751352.
##  4 cycle 3  15600 289083060.
##  5 cycle 4  15600 246928004.
##  6 cycle 5  15600 162935763.
##  7 cycle 6  15600  91343281.
##  8 cycle 7  15600  28250082.
##  9 cycle 8  15600   6904060.
## 10 cycle 9  15600    934255.
## 11 cycle 10 15600    101688.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 260365891.
##  4 cycle 3  15600 287255741.
##  5 cycle 4  15600 246815705.
##  6 cycle 5  15600 160812678.
##  7 cycle 6  15600  89211541.
##  8 cycle 7  15600  28469276.
##  9 cycle 8  15600   6988537.
## 10 cycle 9  15600   1112208.
## 11 cycle 10 15600    165242.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 259124744.
##  4 cycle 3  15600 287306110.
##  5 cycle 4  15600 243314102.
##  6 cycle 5  15600 158448762.
##  7 cycle 6  15600  87452172.
##  8 cycle 7  15600  27209008.
##  9 cycle 8  15600   6634432.
## 10 cycle 9  15600   1004165.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 260311742.
##  4 cycle 3  15600 286540817.
##  5 cycle 4  15600 242452611.
##  6 cycle 5  15600 158423391.
##  7 cycle 6  15600  87076157.
##  8 cycle 7  15600  26300701.
##  9 cycle 8  15600   6184993.
## 10 cycle 9  15600    978743.
## 11 cycle 10 15600    152531.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284389309.
##  3 cycle 2  15600 258517708.
##  4 cycle 3  15600 286125109.
##  5 cycle 4  15600 243983904.
##  6 cycle 5  15600 160276466.
##  7 cycle 6  15600  89184457.
##  8 cycle 7  15600  27968956.
##  9 cycle 8  15600   6673701.
## 10 cycle 9  15600    845278.
## 11 cycle 10 15600    158887.
## 12 cycle 11 15600     50844.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 260041985.
##  4 cycle 3  15600 287851498.
##  5 cycle 4  15600 242145570.
##  6 cycle 5  15600 157521247.
##  7 cycle 6  15600  87834985.
##  8 cycle 7  15600  27338750.
##  9 cycle 8  15600   6393004.
## 10 cycle 9  15600   1055009.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600      6355.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 261479498.
##  4 cycle 3  15600 287736288.
##  5 cycle 4  15600 244965367.
##  6 cycle 5  15600 159700920.
##  7 cycle 6  15600  89405288.
##  8 cycle 7  15600  29002364.
##  9 cycle 8  15600   7067075.
## 10 cycle 9  15600   1048654.
## 11 cycle 10 15600    133465.
## 12 cycle 11 15600     12711.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 260174907.
##  4 cycle 3  15600 288035524.
##  5 cycle 4  15600 245569063.
##  6 cycle 5  15600 157154715.
##  7 cycle 6  15600  88337582.
##  8 cycle 7  15600  26835576.
##  9 cycle 8  15600   6347498.
## 10 cycle 9  15600    883411.
## 11 cycle 10 15600     76266.
## 12 cycle 11 15600         0 
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 253421712.
##  2 cycle 1  15600 285348368.
##  3 cycle 2  15600 258423276.
##  4 cycle 3  15600 288733244.
##  5 cycle 4  15600 248409530.
##  6 cycle 5  15600 161441571.
##  7 cycle 6  15600  88688109.
##  8 cycle 7  15600  27272610.
##  9 cycle 8  15600   6687160.
## 10 cycle 9  15600    902478.
## 11 cycle 10 15600    177953.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0

The same reasoning is applied to female patients:

m.M <- m.C <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_females
#Females
Probs <- function(state){
  return(transition_prob_f[[state]])
}
Costs <- function(state) {
  return(transition_costs_f[[state]])
}
# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_f <- transition_prob_f %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_f[[10]][[current_state]]), 1, prob = transition_prob_f[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_f[[t]][[current_state]]), 1, prob = transition_prob_f[[t]][[current_state]])
         }
        m.M[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M)
}


# Init m.M #repeat it!!!!
model_results_f <- list()
for(i in 1:n.sim) {
m.M <- m.C <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_females
# Microsim loop
model_results_f[[i]] <- loop_microsim(n.t)
print(i)
}  
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15
## [1] 16
## [1] 17
## [1] 18
## [1] 19
## [1] 20
## [1] 21
## [1] 22
## [1] 23
## [1] 24
## [1] 25
## [1] 26
## [1] 27
## [1] 28
## [1] 29
## [1] 30
## [1] 31
## [1] 32
## [1] 33
## [1] 34
## [1] 35
## [1] 36
## [1] 37
## [1] 38
## [1] 39
## [1] 40
## [1] 41
## [1] 42
## [1] 43
## [1] 44
## [1] 45
## [1] 46
## [1] 47
## [1] 48
## [1] 49
## [1] 50
## [1] 51
## [1] 52
## [1] 53
## [1] 54
## [1] 55
## [1] 56
## [1] 57
## [1] 58
## [1] 59
## [1] 60
## [1] 61
## [1] 62
## [1] 63
## [1] 64
## [1] 65
## [1] 66
## [1] 67
## [1] 68
## [1] 69
## [1] 70
## [1] 71
## [1] 72
## [1] 73
## [1] 74
## [1] 75
## [1] 76
## [1] 77
## [1] 78
## [1] 79
## [1] 80
## [1] 81
## [1] 82
## [1] 83
## [1] 84
## [1] 85
## [1] 86
## [1] 87
## [1] 88
## [1] 89
## [1] 90
## [1] 91
## [1] 92
## [1] 93
## [1] 94
## [1] 95
## [1] 96
## [1] 97
## [1] 98
## [1] 99
## [1] 100
# repeat it!!!

#Results of the median cycle, the 50th
model_results_f[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 2   "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 3   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 5   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 7   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 8   "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 9   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 11  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 15  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 17  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 18  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 19  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 22  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 26  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 30  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 31  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 32  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 33  "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 38  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 43  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 48  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 53  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 55  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 60  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 61  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 64  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 65  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 66  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 70  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 71  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 72  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 74  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 76  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 80  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 81  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 82  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 85  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 87  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 90  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 92  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 94  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 96  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 97  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 98  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 101 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 102 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 104 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 105 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"  
## ind 107 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 108 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 112 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 116 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 117 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 120 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 124 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 125 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 128 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 129 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 132 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 133 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 135 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 137 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 139 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 141 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 143 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 145 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"  
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 152 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 155 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 156 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 164 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 165 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 166 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 168 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 173 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 174 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 178 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 179 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 182 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 188 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 190 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 196 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 197 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 198 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 203 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 204 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 206 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 207 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 208 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 209 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 211 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 213 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 214 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 217 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 220 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 224 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 228 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 230 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 233 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 235 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 238 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 239 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 241 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 243 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 244 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 250 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 255 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 258 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 259 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 267 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 268 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 269 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 270 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 272 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 273 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 278 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 279 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 281 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 287 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 294 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 296 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 299 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 2   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 20  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 21  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 22  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 23  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 24  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 25  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 26  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 27  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 28  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 29  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 30  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 31  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 32  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 33  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 50  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 277 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 278 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 279 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 280 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 281 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 282 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 283 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 284 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 285 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 286 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 287 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 288 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 289 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 290 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 291 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 292 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 293 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 294 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 295 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 296 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 297 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 298 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 299 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 300 "D"     "D"      "D"      "D"      "D"      "D"      "D"
df_m.M <- model_results_f[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 10400       1
## 
## [[2]]
##  cycle 1     n percent
##        D   143 0.01375
##      MPD 10257 0.98625
## 
## [[3]]
##  cycle 2    n    percent
##      APD  396 0.03807692
##        D  675 0.06490385
##      MPD 9329 0.89701923
## 
## [[4]]
##  cycle 3    n    percent
##      APD  894 0.08596154
##        D 1158 0.11134615
##      MPD 8348 0.80269231
## 
## [[5]]
##  cycle 4    n   percent
##      APD 1110 0.1067308
##        D 2257 0.2170192
##      MPD 7033 0.6762500
## 
## [[6]]
##  cycle 5    n   percent
##      APD 1217 0.1170192
##        D 3588 0.3450000
##      MPD 5595 0.5379808
## 
## [[7]]
##  cycle 6    n    percent
##      APD 1037 0.09971154
##        D 5328 0.51230769
##      MPD 4035 0.38798077
## 
## [[8]]
##  cycle 7    n    percent
##      APD  648 0.06230769
##        D 7258 0.69788462
##      MPD 2494 0.23980769
## 
## [[9]]
##  cycle 8    n    percent
##      APD  284 0.02730769
##        D 8858 0.85173077
##      MPD 1258 0.12096154
## 
## [[10]]
##  cycle 9    n     percent
##      APD   71 0.006826923
##        D 9912 0.953076923
##      MPD  417 0.040096154
## 
## [[11]]
##  cycle 10     n     percent
##       APD    22 0.002115385
##         D 10262 0.986730769
##       MPD   116 0.011153846
## 
## [[12]]
##  cycle 11     n      percent
##       APD     6 0.0005769231
##         D 10358 0.9959615385
##       MPD    36 0.0034615385
## 
## [[13]]
##  cycle 12     n      percent
##       APD     3 0.0002884615
##         D 10386 0.9986538462
##       MPD    11 0.0010576923
## 
## [[14]]
##  cycle 13     n      percent
##         D 10396 0.9996153846
##       MPD     4 0.0003846154
## 
## [[15]]
##  cycle 14     n       percent
##         D 10399 0.99990384615
##       MPD     1 0.00009615385
#Transition costs
transition_costs_f <-
  transition_costs_f %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
    "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")

final_cost_f <- map(
    model_results_f,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_f
      )
  )
 

final_cost_f2 <-
  map(
    final_cost_f,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
    filter(cycle != "cycle 15")
  )
final_cost_f2
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 180474819.
##  4 cycle 3  10400 172250747.
##  5 cycle 4  10400 191209925.
##  6 cycle 5  10400 152805281.
##  7 cycle 6  10400 122874277.
##  8 cycle 7  10400  53306552.
##  9 cycle 8  10400  12551212.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 179108178.
##  4 cycle 3  10400 170727091.
##  5 cycle 4  10400 189999137.
##  6 cycle 5  10400 152487208.
##  7 cycle 6  10400 120779195.
##  8 cycle 7  10400  54169615.
##  9 cycle 8  10400  13372295.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 181412224.
##  4 cycle 3  10400 172478202.
##  5 cycle 4  10400 190225713.
##  6 cycle 5  10400 153318616.
##  7 cycle 6  10400 121620062.
##  8 cycle 7  10400  53896047.
##  9 cycle 8  10400  13702891.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 181128695.
##  4 cycle 3  10400 172359599.
##  5 cycle 4  10400 190693846.
##  6 cycle 5  10400 152978550.
##  7 cycle 6  10400 122605586.
##  8 cycle 7  10400  52398894.
##  9 cycle 8  10400  12300236.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    105242.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 180298954.
##  4 cycle 3  10400 172363814.
##  5 cycle 4  10400 189786852.
##  6 cycle 5  10400 152757018.
##  7 cycle 6  10400 118668391.
##  8 cycle 7  10400  52660560.
##  9 cycle 8  10400  13490958.
## 10 cycle 9  10400   1110884.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 181658313.
##  4 cycle 3  10400 172541460.
##  5 cycle 4  10400 191903495.
##  6 cycle 5  10400 154237947.
##  7 cycle 6  10400 120826168.
##  8 cycle 7  10400  53083541.
##  9 cycle 8  10400  11975601.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 181704858.
##  4 cycle 3  10400 172487163.
##  5 cycle 4  10400 190826154.
##  6 cycle 5  10400 154196572.
##  7 cycle 6  10400 121156780.
##  8 cycle 7  10400  52797341.
##  9 cycle 8  10400  13044224.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249629414.
##  3 cycle 2  10400 182529943.
##  4 cycle 3  10400 173588329.
##  5 cycle 4  10400 192243531.
##  6 cycle 5  10400 153878483.
##  7 cycle 6  10400 122308994.
##  8 cycle 7  10400  54105682.
##  9 cycle 8  10400  13488157.
## 10 cycle 9  10400    993949.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 181494792.
##  4 cycle 3  10400 171841963.
##  5 cycle 4  10400 192302695.
##  6 cycle 5  10400 155883527.
##  7 cycle 6  10400 122539540.
##  8 cycle 7  10400  53953290.
##  9 cycle 8  10400  13194520.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250506674.
##  3 cycle 2  10400 182754177.
##  4 cycle 3  10400 172874866.
##  5 cycle 4  10400 193871883.
##  6 cycle 5  10400 156059358.
##  7 cycle 6  10400 124470701.
##  8 cycle 7  10400  55921003.
##  9 cycle 8  10400  13654904.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 181590111.
##  4 cycle 3  10400 170445605.
##  5 cycle 4  10400 190606067.
##  6 cycle 5  10400 153409135.
##  7 cycle 6  10400 120545492.
##  8 cycle 7  10400  52798829.
##  9 cycle 8  10400  12407513.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 180534115.
##  4 cycle 3  10400 171902054.
##  5 cycle 4  10400 191393112.
##  6 cycle 5  10400 152802270.
##  7 cycle 6  10400 121646609.
##  8 cycle 7  10400  55147145.
##  9 cycle 8  10400  13865843.
## 10 cycle 9  10400   1052417.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 181261048.
##  4 cycle 3  10400 172112112.
##  5 cycle 4  10400 190472620.
##  6 cycle 5  10400 152202758.
##  7 cycle 6  10400 120215073.
##  8 cycle 7  10400  52324558.
##  9 cycle 8  10400  13104052.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249434467.
##  3 cycle 2  10400 180350155.
##  4 cycle 3  10400 172161662.
##  5 cycle 4  10400 190853457.
##  6 cycle 5  10400 152370420.
##  7 cycle 6  10400 122380324.
##  8 cycle 7  10400  53642559.
##  9 cycle 8  10400  14520357.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 181743309.
##  4 cycle 3  10400 172829531.
##  5 cycle 4  10400 192012710.
##  6 cycle 5  10400 152582036.
##  7 cycle 6  10400 122204417.
##  8 cycle 7  10400  53863338.
##  9 cycle 8  10400  13562808.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 180857511.
##  4 cycle 3  10400 173142380.
##  5 cycle 4  10400 191836668.
##  6 cycle 5  10400 155401668.
##  7 cycle 6  10400 120930938.
##  8 cycle 7  10400  54676590.
##  9 cycle 8  10400  14056177.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 181173824.
##  4 cycle 3  10400 172247847.
##  5 cycle 4  10400 190268792.
##  6 cycle 5  10400 154013821.
##  7 cycle 6  10400 123879646.
##  8 cycle 7  10400  54408983.
##  9 cycle 8  10400  14497586.
## 10 cycle 9  10400   1134271.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 179920919.
##  4 cycle 3  10400 172499025.
##  5 cycle 4  10400 191792312.
##  6 cycle 5  10400 153443605.
##  7 cycle 6  10400 119867578.
##  8 cycle 7  10400  52695496.
##  9 cycle 8  10400  12793605.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249726887.
##  3 cycle 2  10400 183745815.
##  4 cycle 3  10400 173262828.
##  5 cycle 4  10400 192053510.
##  6 cycle 5  10400 152672971.
##  7 cycle 6  10400 121986436.
##  8 cycle 7  10400  53635122.
##  9 cycle 8  10400  13346445.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 181211670.
##  4 cycle 3  10400 173961535.
##  5 cycle 4  10400 191463321.
##  6 cycle 5  10400 153603515.
##  7 cycle 6  10400 122094750.
##  8 cycle 7  10400  52807747.
##  9 cycle 8  10400  12927727.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249751255.
##  3 cycle 2  10400 179975558.
##  4 cycle 3  10400 172975282.
##  5 cycle 4  10400 191600185.
##  6 cycle 5  10400 153266893.
##  7 cycle 6  10400 123305922.
##  8 cycle 7  10400  54838646.
##  9 cycle 8  10400  13667284.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 181240609.
##  4 cycle 3  10400 173265463.
##  5 cycle 4  10400 191482824.
##  6 cycle 5  10400 154010377.
##  7 cycle 6  10400 123450577.
##  8 cycle 7  10400  54603740.
##  9 cycle 8  10400  13613059.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400         0 
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 181067577.
##  4 cycle 3  10400 172910444.
##  5 cycle 4  10400 189206086.
##  6 cycle 5  10400 151182992.
##  7 cycle 6  10400 119085281.
##  8 cycle 7  10400  53636610.
##  9 cycle 8  10400  13166324.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 181349689.
##  4 cycle 3  10400 170924762.
##  5 cycle 4  10400 190237587.
##  6 cycle 5  10400 152313507.
##  7 cycle 6  10400 121137514.
##  8 cycle 7  10400  53531050.
##  9 cycle 8  10400  12841054.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    128629.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 180222053.
##  4 cycle 3  10400 172850353.
##  5 cycle 4  10400 190473276.
##  6 cycle 5  10400 152264812.
##  7 cycle 6  10400 122932848.
##  8 cycle 7  10400  55188777.
##  9 cycle 8  10400  13404286.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 179439668.
##  4 cycle 3  10400 172001945.
##  5 cycle 4  10400 191543300.
##  6 cycle 5  10400 153053547.
##  7 cycle 6  10400 121713234.
##  8 cycle 7  10400  53940652.
##  9 cycle 8  10400  14085644.
## 10 cycle 9  10400   1099191.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 180650685.
##  4 cycle 3  10400 172179850.
##  5 cycle 4  10400 190654668.
##  6 cycle 5  10400 152098447.
##  7 cycle 6  10400 122236442.
##  8 cycle 7  10400  53797181.
##  9 cycle 8  10400  13571034.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 182019754.
##  4 cycle 3  10400 173439153.
##  5 cycle 4  10400 192939207.
##  6 cycle 5  10400 153292330.
##  7 cycle 6  10400 122363828.
##  8 cycle 7  10400  54965022.
##  9 cycle 8  10400  13509576.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 180266170.
##  4 cycle 3  10400 173270999.
##  5 cycle 4  10400 191725175.
##  6 cycle 5  10400 153363883.
##  7 cycle 6  10400 122431614.
##  8 cycle 7  10400  53609853.
##  9 cycle 8  10400  13195155.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 181564005.
##  4 cycle 3  10400 170153843.
##  5 cycle 4  10400 190646557.
##  6 cycle 5  10400 153423342.
##  7 cycle 6  10400 123348577.
##  8 cycle 7  10400  54441681.
##  9 cycle 8  10400  13877050.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249507572.
##  3 cycle 2  10400 179955119.
##  4 cycle 3  10400 172319800.
##  5 cycle 4  10400 189160902.
##  6 cycle 5  10400 151211008.
##  7 cycle 6  10400 120046834.
##  8 cycle 7  10400  53011432.
##  9 cycle 8  10400  13289597.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 182215048.
##  4 cycle 3  10400 172796322.
##  5 cycle 4  10400 189708668.
##  6 cycle 5  10400 151737253.
##  7 cycle 6  10400 120680738.
##  8 cycle 7  10400  54679564.
##  9 cycle 8  10400  13844424.
## 10 cycle 9  10400   1087497.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 180463485.
##  4 cycle 3  10400 172171679.
##  5 cycle 4  10400 191565564.
##  6 cycle 5  10400 154440080.
##  7 cycle 6  10400 121986049.
##  8 cycle 7  10400  54672134.
##  9 cycle 8  10400  14432333.
## 10 cycle 9  10400   1040723.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249873097.
##  3 cycle 2  10400 179825194.
##  4 cycle 3  10400 171980068.
##  5 cycle 4  10400 190700508.
##  6 cycle 5  10400 153086304.
##  7 cycle 6  10400 122297976.
##  8 cycle 7  10400  53016635.
##  9 cycle 8  10400  12236156.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 181566433.
##  4 cycle 3  10400 173949408.
##  5 cycle 4  10400 190099412.
##  6 cycle 5  10400 152641544.
##  7 cycle 6  10400 118612915.
##  8 cycle 7  10400  51969971.
##  9 cycle 8  10400  12875131.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 180111150.
##  4 cycle 3  10400 172150856.
##  5 cycle 4  10400 190983659.
##  6 cycle 5  10400 150434334.
##  7 cycle 6  10400 119096879.
##  8 cycle 7  10400  52127563.
##  9 cycle 8  10400  12394141.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 181522721.
##  4 cycle 3  10400 173833706.
##  5 cycle 4  10400 191113205.
##  6 cycle 5  10400 152791489.
##  7 cycle 6  10400 122363441.
##  8 cycle 7  10400  54866897.
##  9 cycle 8  10400  13827614.
## 10 cycle 9  10400   1169352.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 181826283.
##  4 cycle 3  10400 173541413.
##  5 cycle 4  10400 191585066.
##  6 cycle 5  10400 153519035.
##  7 cycle 6  10400 121850416.
##  8 cycle 7  10400  53214376.
##  9 cycle 8  10400  12963334.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 182040193.
##  4 cycle 3  10400 172042004.
##  5 cycle 4  10400 188462293.
##  6 cycle 5  10400 149691283.
##  7 cycle 6  10400 119956819.
##  8 cycle 7  10400  52690296.
##  9 cycle 8  10400  12391518.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    152016.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 182849089.
##  4 cycle 3  10400 172181430.
##  5 cycle 4  10400 191496321.
##  6 cycle 5  10400 153848321.
##  7 cycle 6  10400 122107315.
##  8 cycle 7  10400  54324233.
##  9 cycle 8  10400  12907479.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    140322.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 181905007.
##  4 cycle 3  10400 173488438.
##  5 cycle 4  10400 191155455.
##  6 cycle 5  10400 153121224.
##  7 cycle 6  10400 121636558.
##  8 cycle 7  10400  52024977.
##  9 cycle 8  10400  12957551.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 180448714.
##  4 cycle 3  10400 171365441.
##  5 cycle 4  10400 191787445.
##  6 cycle 5  10400 154241823.
##  7 cycle 6  10400 123240844.
##  8 cycle 7  10400  54185223.
##  9 cycle 8  10400  13329635.
## 10 cycle 9  10400   1169352.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249361362.
##  3 cycle 2  10400 177937435.
##  4 cycle 3  10400 171815870.
##  5 cycle 4  10400 191132708.
##  6 cycle 5  10400 152950966.
##  7 cycle 6  10400 122149777.
##  8 cycle 7  10400  53678981.
##  9 cycle 8  10400  13338040.
## 10 cycle 9  10400    783466.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250555411.
##  3 cycle 2  10400 182294178.
##  4 cycle 3  10400 172867485.
##  5 cycle 4  10400 190956839.
##  6 cycle 5  10400 153313009.
##  7 cycle 6  10400 121398344.
##  8 cycle 7  10400  54264764.
##  9 cycle 8  10400  12740373.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    397580.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 180444057.
##  4 cycle 3  10400 172655317.
##  5 cycle 4  10400 190791532.
##  6 cycle 5  10400 152000175.
##  7 cycle 6  10400 120918179.
##  8 cycle 7  10400  53306552.
##  9 cycle 8  10400  13583234.
## 10 cycle 9  10400   1087497.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 180485747.
##  4 cycle 3  10400 171771590.
##  5 cycle 4  10400 189348473.
##  6 cycle 5  10400 151362300.
##  7 cycle 6  10400 118769617.
##  8 cycle 7  10400  52050993.
##  9 cycle 8  10400  13087242.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 179670173.
##  4 cycle 3  10400 170833042.
##  5 cycle 4  10400 190069036.
##  6 cycle 5  10400 151692434.
##  7 cycle 6  10400 119571373.
##  8 cycle 7  10400  52711109.
##  9 cycle 8  10400  12634904.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 180856094.
##  4 cycle 3  10400 171210729.
##  5 cycle 4  10400 190358677.
##  6 cycle 5  10400 153417320.
##  7 cycle 6  10400 122352036.
##  8 cycle 7  10400  52970547.
##  9 cycle 8  10400  13150687.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 182846256.
##  4 cycle 3  10400 174011610.
##  5 cycle 4  10400 191844642.
##  6 cycle 5  10400 154703003.
##  7 cycle 6  10400 121616132.
##  8 cycle 7  10400  53844013.
##  9 cycle 8  10400  12707747.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 180538365.
##  4 cycle 3  10400 172617628.
##  5 cycle 4  10400 191614648.
##  6 cycle 5  10400 154571974.
##  7 cycle 6  10400 122951920.
##  8 cycle 7  10400  54157719.
##  9 cycle 8  10400  13586036.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 182261999.
##  4 cycle 3  10400 174083038.
##  5 cycle 4  10400 191907395.
##  6 cycle 5  10400 152518235.
##  7 cycle 6  10400 121736624.
##  8 cycle 7  10400  54159202.
##  9 cycle 8  10400  13848040.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 182813472.
##  4 cycle 3  10400 174236695.
##  5 cycle 4  10400 192439559.
##  6 cycle 5  10400 154666785.
##  7 cycle 6  10400 123518812.
##  8 cycle 7  10400  54685508.
##  9 cycle 8  10400  13288961.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249775624.
##  3 cycle 2  10400 181472124.
##  4 cycle 3  10400 172288961.
##  5 cycle 4  10400 189193729.
##  6 cycle 5  10400 150346427.
##  7 cycle 6  10400 119363188.
##  8 cycle 7  10400  52844176.
##  9 cycle 8  10400  12743990.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249385730.
##  3 cycle 2  10400 179643661.
##  4 cycle 3  10400 171580503.
##  5 cycle 4  10400 190675793.
##  6 cycle 5  10400 153798328.
##  7 cycle 6  10400 122733745.
##  8 cycle 7  10400  53904967.
##  9 cycle 8  10400  14000144.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 184796550.
##  4 cycle 3  10400 172260498.
##  5 cycle 4  10400 192227101.
##  6 cycle 5  10400 154598709.
##  7 cycle 6  10400 121465743.
##  8 cycle 7  10400  53162341.
##  9 cycle 8  10400  13843251.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 180270421.
##  4 cycle 3  10400 170961927.
##  5 cycle 4  10400 191703394.
##  6 cycle 5  10400 155000397.
##  7 cycle 6  10400 121811884.
##  8 cycle 7  10400  54418644.
##  9 cycle 8  10400  14204128.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 181005042.
##  4 cycle 3  10400 172261288.
##  5 cycle 4  10400 191122629.
##  6 cycle 5  10400 153059586.
##  7 cycle 6  10400 120619785.
##  8 cycle 7  10400  52925949.
##  9 cycle 8  10400  13682742.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400    163709.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250457938.
##  3 cycle 2  10400 180038499.
##  4 cycle 3  10400 171180421.
##  5 cycle 4  10400 191477474.
##  6 cycle 5  10400 155046929.
##  7 cycle 6  10400 123655799.
##  8 cycle 7  10400  55101057.
##  9 cycle 8  10400  14265764.
## 10 cycle 9  10400   1181045.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 180009560.
##  4 cycle 3  10400 171196497.
##  5 cycle 4  10400 191239334.
##  6 cycle 5  10400 152387655.
##  7 cycle 6  10400 119183157.
##  8 cycle 7  10400  52566152.
##  9 cycle 8  10400  13685723.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 181531826.
##  4 cycle 3  10400 173775984.
##  5 cycle 4  10400 192187923.
##  6 cycle 5  10400 152760479.
##  7 cycle 6  10400 120090263.
##  8 cycle 7  10400  52747536.
##  9 cycle 8  10400  12941556.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 180656956.
##  4 cycle 3  10400 173199836.
##  5 cycle 4  10400 191404468.
##  6 cycle 5  10400 153997034.
##  7 cycle 6  10400 123284467.
##  8 cycle 7  10400  54955361.
##  9 cycle 8  10400  14281402.
## 10 cycle 9  10400   1087497.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 181204992.
##  4 cycle 3  10400 171432913.
##  5 cycle 4  10400 192869653.
##  6 cycle 5  10400 154587912.
##  7 cycle 6  10400 121771612.
##  8 cycle 7  10400  53042651.
##  9 cycle 8  10400  12927270.
## 10 cycle 9  10400   1099191.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250652885.
##  3 cycle 2  10400 179309743.
##  4 cycle 3  10400 170726301.
##  5 cycle 4  10400 190417530.
##  6 cycle 5  10400 153239726.
##  7 cycle 6  10400 122332190.
##  8 cycle 7  10400  54616375.
##  9 cycle 8  10400  14757326.
## 10 cycle 9  10400   1099191.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 180686302.
##  4 cycle 3  10400 172609457.
##  5 cycle 4  10400 192448327.
##  6 cycle 5  10400 154457332.
##  7 cycle 6  10400 123084977.
##  8 cycle 7  10400  53905714.
##  9 cycle 8  10400  13547806.
## 10 cycle 9  10400   1157658.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 181804427.
##  4 cycle 3  10400 173634189.
##  5 cycle 4  10400 191403018.
##  6 cycle 5  10400 152695813.
##  7 cycle 6  10400 123750519.
##  8 cycle 7  10400  55250475.
##  9 cycle 8  10400  14210725.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 179924763.
##  4 cycle 3  10400 170747123.
##  5 cycle 4  10400 191996314.
##  6 cycle 5  10400 153947009.
##  7 cycle 6  10400 122447978.
##  8 cycle 7  10400  55672710.
##  9 cycle 8  10400  14342224.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 181508554.
##  4 cycle 3  10400 171823776.
##  5 cycle 4  10400 192554297.
##  6 cycle 5  10400 152895816.
##  7 cycle 6  10400 119517894.
##  8 cycle 7  10400  53779341.
##  9 cycle 8  10400  12470958.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 181011115.
##  4 cycle 3  10400 173511895.
##  5 cycle 4  10400 191865767.
##  6 cycle 5  10400 153351822.
##  7 cycle 6  10400 121302209.
##  8 cycle 7  10400  53751837.
##  9 cycle 8  10400  13150508.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 181522315.
##  4 cycle 3  10400 173046969.
##  5 cycle 4  10400 191373436.
##  6 cycle 5  10400 153132420.
##  7 cycle 6  10400 122944833.
##  8 cycle 7  10400  53450768.
##  9 cycle 8  10400  13610257.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400         0 
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 180596650.
##  4 cycle 3  10400 172303723.
##  5 cycle 4  10400 190762917.
##  6 cycle 5  10400 151356693.
##  7 cycle 6  10400 119641349.
##  8 cycle 7  10400  52938581.
##  9 cycle 8  10400  13099084.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 180269410.
##  4 cycle 3  10400 172571243.
##  5 cycle 4  10400 191230567.
##  6 cycle 5  10400 150586907.
##  7 cycle 6  10400 121631274.
##  8 cycle 7  10400  54518999.
##  9 cycle 8  10400  14237569.
## 10 cycle 9  10400   1251206.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250482306.
##  3 cycle 2  10400 181372962.
##  4 cycle 3  10400 174353977.
##  5 cycle 4  10400 191917129.
##  6 cycle 5  10400 154749568.
##  7 cycle 6  10400 122396239.
##  8 cycle 7  10400  53493143.
##  9 cycle 8  10400  13258600.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 181347866.
##  4 cycle 3  10400 173407258.
##  5 cycle 4  10400 190930329.
##  6 cycle 5  10400 153066922.
##  7 cycle 6  10400 121049821.
##  8 cycle 7  10400  54055879.
##  9 cycle 8  10400  14404317.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 181905413.
##  4 cycle 3  10400 172667968.
##  5 cycle 4  10400 190858014.
##  6 cycle 5  10400 152055342.
##  7 cycle 6  10400 120973593.
##  8 cycle 7  10400  53939164.
##  9 cycle 8  10400  12913718.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 181471520.
##  4 cycle 3  10400 174086198.
##  5 cycle 4  10400 192163070.
##  6 cycle 5  10400 153266460.
##  7 cycle 6  10400 123290586.
##  8 cycle 7  10400  54545759.
##  9 cycle 8  10400  13837648.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 179798682.
##  4 cycle 3  10400 172271304.
##  5 cycle 4  10400 190208006.
##  6 cycle 5  10400 151609684.
##  7 cycle 6  10400 119574530.
##  8 cycle 7  10400  53522871.
##  9 cycle 8  10400  13402478.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 180062782.
##  4 cycle 3  10400 170688612.
##  5 cycle 4  10400 191033400.
##  6 cycle 5  10400 152477293.
##  7 cycle 6  10400 123142581.
##  8 cycle 7  10400  54553193.
##  9 cycle 8  10400  13540395.
## 10 cycle 9  10400   1040723.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250262991.
##  3 cycle 2  10400 182360151.
##  4 cycle 3  10400 173314224.
##  5 cycle 4  10400 191614476.
##  6 cycle 5  10400 153201828.
##  7 cycle 6  10400 121017022.
##  8 cycle 7  10400  52929664.
##  9 cycle 8  10400  13228775.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 181551661.
##  4 cycle 3  10400 173003220.
##  5 cycle 4  10400 191351656.
##  6 cycle 5  10400 153598774.
##  7 cycle 6  10400 124245633.
##  8 cycle 7  10400  54694436.
##  9 cycle 8  10400  13442336.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 180439202.
##  4 cycle 3  10400 172981873.
##  5 cycle 4  10400 192963439.
##  6 cycle 5  10400 156186943.
##  7 cycle 6  10400 122204417.
##  8 cycle 7  10400  53101381.
##  9 cycle 8  10400  12929714.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 179573438.
##  4 cycle 3  10400 172588634.
##  5 cycle 4  10400 191044445.
##  6 cycle 5  10400 153251388.
##  7 cycle 6  10400 121748802.
##  8 cycle 7  10400  54051418.
##  9 cycle 8  10400  14397899.
## 10 cycle 9  10400   1134271.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 180785465.
##  4 cycle 3  10400 173486592.
##  5 cycle 4  10400 191613509.
##  6 cycle 5  10400 153487576.
##  7 cycle 6  10400 121864784.
##  8 cycle 7  10400  54296727.
##  9 cycle 8  10400  13403471.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 181931925.
##  4 cycle 3  10400 173352178.
##  5 cycle 4  10400 193156049.
##  6 cycle 5  10400 155681377.
##  7 cycle 6  10400 122761065.
##  8 cycle 7  10400  54925622.
##  9 cycle 8  10400  13577631.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 181601039.
##  4 cycle 3  10400 172831110.
##  5 cycle 4  10400 190287466.
##  6 cycle 5  10400 151970878.
##  7 cycle 6  10400 120912446.
##  8 cycle 7  10400  52815928.
##  9 cycle 8  10400  13469360.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 181058472.
##  4 cycle 3  10400 171867266.
##  5 cycle 4  10400 192494339.
##  6 cycle 5  10400 153704366.
##  7 cycle 6  10400 121791845.
##  8 cycle 7  10400  54243951.
##  9 cycle 8  10400  14101639.
## 10 cycle 9  10400   1110884.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 181780749.
##  4 cycle 3  10400 173034583.
##  5 cycle 4  10400 190510659.
##  6 cycle 5  10400 151022217.
##  7 cycle 6  10400 122297396.
##  8 cycle 7  10400  54547244.
##  9 cycle 8  10400  13322223.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 182529943.
##  4 cycle 3  10400 172815299.
##  5 cycle 4  10400 190995188.
##  6 cycle 5  10400 151877348.
##  7 cycle 6  10400 120203281.
##  8 cycle 7  10400  52998797.
##  9 cycle 8  10400  13058232.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    140322.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 182839578.
##  4 cycle 3  10400 172408095.
##  5 cycle 4  10400 190507242.
##  6 cycle 5  10400 151871742.
##  7 cycle 6  10400 118878705.
##  8 cycle 7  10400  53458200.
##  9 cycle 8  10400  14339243.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249483204.
##  3 cycle 2  10400 180488581.
##  4 cycle 3  10400 174104910.
##  5 cycle 4  10400 192189235.
##  6 cycle 5  10400 153291032.
##  7 cycle 6  10400 122360284.
##  8 cycle 7  10400  54750185.
##  9 cycle 8  10400  13479752.
## 10 cycle 9  10400   1075804.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 180580660.
##  4 cycle 3  10400 172267614.
##  5 cycle 4  10400 191443646.
##  6 cycle 5  10400 153058737.
##  7 cycle 6  10400 120304701.
##  8 cycle 7  10400  53374202.
##  9 cycle 8  10400  13839814.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 179611284.
##  4 cycle 3  10400 171896784.
##  5 cycle 4  10400 191756068.
##  6 cycle 5  10400 154034932.
##  7 cycle 6  10400 121746868.
##  8 cycle 7  10400  54676590.
##  9 cycle 8  10400  13419288.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 182099290.
##  4 cycle 3  10400 172849828.
##  5 cycle 4  10400 193504543.
##  6 cycle 5  10400 154640500.
##  7 cycle 6  10400 123029756.
##  8 cycle 7  10400  54505615.
##  9 cycle 8  10400  13223530.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 180660196.
##  4 cycle 3  10400 172248112.
##  5 cycle 4  10400 191176753.
##  6 cycle 5  10400 153422926.
##  7 cycle 6  10400 122438895.
##  8 cycle 7  10400  53194306.
##  9 cycle 8  10400  13697923.
## 10 cycle 9  10400   1169352.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 180939069.
##  4 cycle 3  10400 172528277.
##  5 cycle 4  10400 190527884.
##  6 cycle 5  10400 152846672.
##  7 cycle 6  10400 119265118.
##  8 cycle 7  10400  51903065.
##  9 cycle 8  10400  13079652.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249653782.
##  3 cycle 2  10400 180246742.
##  4 cycle 3  10400 172642665.
##  5 cycle 4  10400 189889094.
##  6 cycle 5  10400 151559259.
##  7 cycle 6  10400 120053921.
##  8 cycle 7  10400  52645691.
##  9 cycle 8  10400  12497981.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 182340723.
##  4 cycle 3  10400 173832126.
##  5 cycle 4  10400 192145362.
##  6 cycle 5  10400 153192744.
##  7 cycle 6  10400 123675646.
##  8 cycle 7  10400  53901991.
##  9 cycle 8  10400  14029154.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 180631257.
##  4 cycle 3  10400 171014902.
##  5 cycle 4  10400 189921127.
##  6 cycle 5  10400 153106983.
##  7 cycle 6  10400 124380299.
##  8 cycle 7  10400  54973937.
##  9 cycle 8  10400  12996953.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 181273393.
##  4 cycle 3  10400 171801108.
##  5 cycle 4  10400 191107510.
##  6 cycle 5  10400 152081644.
##  7 cycle 6  10400 121002267.
##  8 cycle 7  10400  54006812.
##  9 cycle 8  10400  13492766.
## 10 cycle 9  10400   1099191.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 181865546.
##  4 cycle 3  10400 174185034.
##  5 cycle 4  10400 191311822.
##  6 cycle 5  10400 152007512.
##  7 cycle 6  10400 120288979.
##  8 cycle 7  10400  52337936.
##  9 cycle 8  10400  13519431.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 160238564.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 181128695.
##  4 cycle 3  10400 172017232.
##  5 cycle 4  10400 192671831.
##  6 cycle 5  10400 154914619.
##  7 cycle 6  10400 124571347.
##  8 cycle 7  10400  55434830.
##  9 cycle 8  10400  14034578.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0

The variability of costs over 30 simulations is observed through a box plot:

#Males
final_cost_m2_combined <- bind_rows(final_cost_m2)

final_cost_m2_combined$cycle <- factor(final_cost_m2_combined$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_m <- ggplot(final_cost_m2_combined, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Baseline Scenario (Males)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m

#Females
final_cost_f2_combined <- bind_rows(final_cost_f2)

final_cost_f2_combined$cycle <- factor(final_cost_f2_combined$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_f <- ggplot(final_cost_f2_combined, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Baseline Scenario (Females)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f

Both graphs show a roughly decreasing trend in total costs over cycles. “Cycle 0” starts with the highest total costs for both genders, since all patients are alive at the prodromal stage and they are associated with the average costs of “healthy” patients. Then, costs drop significantly by “cycle 5” for males and by “cycle 7” for females due to the higher longevity of female patients. However, the most important remark is that variability ap-pears to be moderate across microsimulations, especially among the latest cycles whereby a rapid stabilization of costs occurs.

The graphs showcasing costs over cycles are:

#Averaging costs across simulations
#Males
combined_costs_m <- map_df(final_cost_m2, ~ .x)
mean_costs_per_cycle_m <- combined_costs_m %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_m)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0    253421712. 
##  2 cycle 1    284876172. 
##  3 cycle 2    259729507. 
##  4 cycle 3    287526642. 
##  5 cycle 4    243901820. 
##  6 cycle 5    158629855. 
##  7 cycle 6     88224439. 
##  8 cycle 7     27382310. 
##  9 cycle 8      6501758. 
## 10 cycle 9       944742. 
## 11 cycle 10      143824. 
## 12 cycle 11       22117. 
## 13 cycle 12        3178. 
## 14 cycle 13         445. 
## 15 cycle 14          63.6
#Females
combined_costs_f <- map_df(final_cost_f2, ~ .x)
mean_costs_per_cycle_f <- combined_costs_f %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_f)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0     160238564.
##  2 cycle 1     249974470.
##  3 cycle 2     181091646.
##  4 cycle 3     172516340.
##  5 cycle 4     191240536.
##  6 cycle 5     153138190.
##  7 cycle 6     121665701.
##  8 cycle 7      53768910.
##  9 cycle 8      13421427.
## 10 cycle 9        957348.
## 11 cycle 10       246616.
## 12 cycle 11        60105.
## 13 cycle 12        16020.
## 14 cycle 13         4327.
## 15 cycle 14          819.
#Graphs
#Males
graph1 <- ggplot(data = mean_costs_per_cycle_m %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "turquoise") +
  ggtitle("Average total costs from microsimulation (Males)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal()+
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
#Females
graph2 <- ggplot(data = mean_costs_per_cycle_f %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "pink") +
  ggtitle("Average total costs from microsimulation (Females)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal()+
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)

graph1

graph2

Both graphs show a similar trend of high initial costs followed by a gradual decline over the microsimulation period and stabilization at minimal values, which cannot even be showcased by the depiction, for both genders. Considering male patients, the highest costs are observed between 2020 and 2040, and the decline starts thereafter. On the other hand, female patients exhibit costs that decline more gradually and remain significant for the 2050-2065 period, when the costs of males really start to disappear from the graph: again, this is evidence for the higher longevity of women.

Costs need to be discounted when it comes to comparing them at time 0, corresponding to 2020, the first year of the model’s time window 2020 – 2095. In accordance with the approach suggested by the Quinet Commission, “d.c.1” is applied to the first 10 rows of “final_cost_m2”, the list of tables containing aggregated costs for each microsimulation, while “d.c.2” is applied to the last 5. In this way, the two discount periods 2020-2070 and 2070-2095 are differentiated. Eventually, discount weights, “dw”, are multiplied by aggregated costs to obtain the column “discounted_costs”.

# Males
discounted_costs_m <-
  map(final_cost_m2, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_m
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       201487813.
##  4 cycle 3  15600       197506065.
##  5 cycle 4  15600       148868590.
##  6 cycle 5  15600        85074813.
##  7 cycle 6  15600        41963584.
##  8 cycle 7  15600        11066131.
##  9 cycle 8  15600         2368204.
## 10 cycle 9  15600          294980.
## 11 cycle 10 15600           54340.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       202752474.
##  4 cycle 3  15600       197869161.
##  5 cycle 4  15600       148702633.
##  6 cycle 5  15600        84791090.
##  7 cycle 6  15600        41751788.
##  8 cycle 7  15600        11783791.
##  9 cycle 8  15600         2535222.
## 10 cycle 9  15600          274060.
## 11 cycle 10 15600           48302.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       203223505.
##  4 cycle 3  15600       198127498.
##  5 cycle 4  15600       148851181.
##  6 cycle 5  15600        84807874.
##  7 cycle 6  15600        41430976.
##  8 cycle 7  15600        11102051.
##  9 cycle 8  15600         2340243.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252006927.
##  3 cycle 2  15600       204799043.
##  4 cycle 3  15600       198581150.
##  5 cycle 4  15600       149003681.
##  6 cycle 5  15600        85398627.
##  7 cycle 6  15600        42264769.
##  8 cycle 7  15600        11663620.
##  9 cycle 8  15600         2500816.
## 10 cycle 9  15600          328453.
## 11 cycle 10 15600           90566.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       203666123.
##  4 cycle 3  15600       200022947.
##  5 cycle 4  15600       148369875.
##  6 cycle 5  15600        85449681.
##  7 cycle 6  15600        42184319.
##  8 cycle 7  15600        11843269.
##  9 cycle 8  15600         2446406.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251973685.
##  3 cycle 2  15600       201625415.
##  4 cycle 3  15600       198772109.
##  5 cycle 4  15600       149060269.
##  6 cycle 5  15600        85888631.
##  7 cycle 6  15600        42682408.
##  8 cycle 7  15600        11443364.
##  9 cycle 8  15600         2338476.
## 10 cycle 9  15600          320085.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252605281.
##  3 cycle 2  15600       202409999.
##  4 cycle 3  15600       198624759.
##  5 cycle 4  15600       148429138.
##  6 cycle 5  15600        86125417.
##  7 cycle 6  15600        43283050.
##  8 cycle 7  15600        12279482.
##  9 cycle 8  15600         2535811.
## 10 cycle 9  15600          274060.
## 11 cycle 10 15600           51321.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       204001846.
##  4 cycle 3  15600       198212115.
##  5 cycle 4  15600       148286897.
##  6 cycle 5  15600        85794396.
##  7 cycle 6  15600        42443046.
##  8 cycle 7  15600        11706000.
##  9 cycle 8  15600         2529254.
## 10 cycle 9  15600          359834.
## 11 cycle 10 15600           45283.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       203736198.
##  4 cycle 3  15600       198879480.
##  5 cycle 4  15600       148267482.
##  6 cycle 5  15600        86283715.
##  7 cycle 6  15600        42287361.
##  8 cycle 7  15600        11736346.
##  9 cycle 8  15600         2394653.
## 10 cycle 9  15600          305441.
## 11 cycle 10 15600           51321.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252405830.
##  3 cycle 2  15600       203769962.
##  4 cycle 3  15600       198246458.
##  5 cycle 4  15600       149232358.
##  6 cycle 5  15600        86457789.
##  7 cycle 6  15600        42552300.
##  8 cycle 7  15600        11958630.
##  9 cycle 8  15600         2333320.
## 10 cycle 9  15600          286612.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       203379963.
##  4 cycle 3  15600       199675505.
##  5 cycle 4  15600       149424037.
##  6 cycle 5  15600        85872855.
##  7 cycle 6  15600        42308217.
##  8 cycle 7  15600        11624592.
##  9 cycle 8  15600         2511717.
## 10 cycle 9  15600          305441.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       203941709.
##  4 cycle 3  15600       200209273.
##  5 cycle 4  15600       149724941.
##  6 cycle 5  15600        86561961.
##  7 cycle 6  15600        42438330.
##  8 cycle 7  15600        11606607.
##  9 cycle 8  15600         2289445.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           84529.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251292226.
##  3 cycle 2  15600       202248061.
##  4 cycle 3  15600       198214293.
##  5 cycle 4  15600       149488938.
##  6 cycle 5  15600        85362302.
##  7 cycle 6  15600        42289355.
##  8 cycle 7  15600        11567396.
##  9 cycle 8  15600         2422391.
## 10 cycle 9  15600          292888.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252306104.
##  3 cycle 2  15600       201637519.
##  4 cycle 3  15600       198734294.
##  5 cycle 4  15600       147256800.
##  6 cycle 5  15600        84807198.
##  7 cycle 6  15600        41886860.
##  8 cycle 7  15600        10962560.
##  9 cycle 8  15600         2377116.
## 10 cycle 9  15600          347282.
## 11 cycle 10 15600           90566.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       203824619.
##  4 cycle 3  15600       199274014.
##  5 cycle 4  15600       150347933.
##  6 cycle 5  15600        85445906.
##  7 cycle 6  15600        42320386.
##  8 cycle 7  15600        11412702.
##  9 cycle 8  15600         2330885.
## 10 cycle 9  15600          317993.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       203344670.
##  4 cycle 3  15600       198121123.
##  5 cycle 4  15600       147905689.
##  6 cycle 5  15600        84717756.
##  7 cycle 6  15600        41300620.
##  8 cycle 7  15600        11328136.
##  9 cycle 8  15600         2475878.
## 10 cycle 9  15600          320085.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       203386843.
##  4 cycle 3  15600       197903649.
##  5 cycle 4  15600       148703127.
##  6 cycle 5  15600        85569943.
##  7 cycle 6  15600        41478650.
##  8 cycle 7  15600        11619783.
##  9 cycle 8  15600         2363891.
## 10 cycle 9  15600          276152.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       201344988.
##  4 cycle 3  15600       197327857.
##  5 cycle 4  15600       147728456.
##  6 cycle 5  15600        85694341.
##  7 cycle 6  15600        42198225.
##  8 cycle 7  15600        11740147.
##  9 cycle 8  15600         2578731.
## 10 cycle 9  15600          334730.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       200065675.
##  4 cycle 3  15600       196723516.
##  5 cycle 4  15600       147984542.
##  6 cycle 5  15600        84776351.
##  7 cycle 6  15600        41785555.
##  8 cycle 7  15600        11682309.
##  9 cycle 8  15600         2349600.
## 10 cycle 9  15600          286612.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251724371.
##  3 cycle 2  15600       202803565.
##  4 cycle 3  15600       199227357.
##  5 cycle 4  15600       149057944.
##  6 cycle 5  15600        85053570.
##  7 cycle 6  15600        41693187.
##  8 cycle 7  15600        11708660.
##  9 cycle 8  15600         2614124.
## 10 cycle 9  15600          282428.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251258984.
##  3 cycle 2  15600       204461154.
##  4 cycle 3  15600       197103281.
##  5 cycle 4  15600       148358918.
##  6 cycle 5  15600        85235165.
##  7 cycle 6  15600        42205421.
##  8 cycle 7  15600        11368865.
##  9 cycle 8  15600         2370670.
## 10 cycle 9  15600          276152.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       204621435.
##  4 cycle 3  15600       200346631.
##  5 cycle 4  15600       150161574.
##  6 cycle 5  15600        87218507.
##  7 cycle 6  15600        42578870.
##  8 cycle 7  15600        11567651.
##  9 cycle 8  15600         2337553.
## 10 cycle 9  15600          317993.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       202659721.
##  4 cycle 3  15600       197928132.
##  5 cycle 4  15600       149650101.
##  6 cycle 5  15600        86150426.
##  7 cycle 6  15600        42670991.
##  8 cycle 7  15600        11642649.
##  9 cycle 8  15600         2386584.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           57359.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251674508.
##  3 cycle 2  15600       202494088.
##  4 cycle 3  15600       197335393.
##  5 cycle 4  15600       147729937.
##  6 cycle 5  15600        85676872.
##  7 cycle 6  15600        41556125.
##  8 cycle 7  15600        11309132.
##  9 cycle 8  15600         2315305.
## 10 cycle 9  15600          328453.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       201979356.
##  4 cycle 3  15600       196764524.
##  5 cycle 4  15600       146776512.
##  6 cycle 5  15600        84638260.
##  7 cycle 6  15600        41938503.
##  8 cycle 7  15600        11718605.
##  9 cycle 8  15600         2406955.
## 10 cycle 9  15600          284520.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       205684280.
##  4 cycle 3  15600       201418667.
##  5 cycle 4  15600       151351495.
##  6 cycle 5  15600        87034154.
##  7 cycle 6  15600        43522659.
##  8 cycle 7  15600        11864556.
##  9 cycle 8  15600         2517796.
## 10 cycle 9  15600          332638.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       202976841.
##  4 cycle 3  15600       198936854.
##  5 cycle 4  15600       147822536.
##  6 cycle 5  15600        85376689.
##  7 cycle 6  15600        41163063.
##  8 cycle 7  15600        11203023.
##  9 cycle 8  15600         2393252.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251790855.
##  3 cycle 2  15600       202162826.
##  4 cycle 3  15600       198706485.
##  5 cycle 4  15600       147374945.
##  6 cycle 5  15600        85132372.
##  7 cycle 6  15600        40905326.
##  8 cycle 7  15600        11307674.
##  9 cycle 8  15600         2348200.
## 10 cycle 9  15600          261508.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       202587098.
##  4 cycle 3  15600       198458574.
##  5 cycle 4  15600       147956639.
##  6 cycle 5  15600        86676070.
##  7 cycle 6  15600        42927481.
##  8 cycle 7  15600        12452986.
##  9 cycle 8  15600         2540123.
## 10 cycle 9  15600          322177.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       202450896.
##  4 cycle 3  15600       198014201.
##  5 cycle 4  15600       147865172.
##  6 cycle 5  15600        83484150.
##  7 cycle 6  15600        40773476.
##  8 cycle 7  15600        11138663.
##  9 cycle 8  15600         2316372.
## 10 cycle 9  15600          292888.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       201580567.
##  4 cycle 3  15600       199461792.
##  5 cycle 4  15600       149841749.
##  6 cycle 5  15600        84864748.
##  7 cycle 6  15600        41337366.
##  8 cycle 7  15600        11413273.
##  9 cycle 8  15600         2306314.
## 10 cycle 9  15600          311717.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       202244495.
##  4 cycle 3  15600       198948733.
##  5 cycle 4  15600       149692418.
##  6 cycle 5  15600        86161390.
##  7 cycle 6  15600        42448015.
##  8 cycle 7  15600        11695800.
##  9 cycle 8  15600         2380028.
## 10 cycle 9  15600          353558.
## 11 cycle 10 15600           87548.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       204804776.
##  4 cycle 3  15600       199751714.
##  5 cycle 4  15600       149633649.
##  6 cycle 5  15600        86148011.
##  7 cycle 6  15600        42153774.
##  8 cycle 7  15600        11570372.
##  9 cycle 8  15600         2503283.
## 10 cycle 9  15600          282428.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       203927439.
##  4 cycle 3  15600       199531917.
##  5 cycle 4  15600       148936629.
##  6 cycle 5  15600        84729062.
##  7 cycle 6  15600        41419802.
##  8 cycle 7  15600        11282526.
##  9 cycle 8  15600         2481146.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       203267844.
##  4 cycle 3  15600       199230103.
##  5 cycle 4  15600       151001679.
##  6 cycle 5  15600        87071506.
##  7 cycle 6  15600        42231997.
##  8 cycle 7  15600        11887046.
##  9 cycle 8  15600         2661278.
## 10 cycle 9  15600          343098.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       203933809.
##  4 cycle 3  15600       200074963.
##  5 cycle 4  15600       150719346.
##  6 cycle 5  15600        85237220.
##  7 cycle 6  15600        41933782.
##  8 cycle 7  15600        11496382.
##  9 cycle 8  15600         2529032.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           54340.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       200931290.
##  4 cycle 3  15600       196608766.
##  5 cycle 4  15600       147857733.
##  6 cycle 5  15600        84768126.
##  7 cycle 6  15600        41330917.
##  8 cycle 7  15600        11313248.
##  9 cycle 8  15600         2371737.
## 10 cycle 9  15600          315901.
## 11 cycle 10 15600           81510.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       203596558.
##  4 cycle 3  15600       199504529.
##  5 cycle 4  15600       149943679.
##  6 cycle 5  15600        85314328.
##  7 cycle 6  15600        42637214.
##  8 cycle 7  15600        11729121.
##  9 cycle 8  15600         2405299.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252389209.
##  3 cycle 2  15600       202397386.
##  4 cycle 3  15600       201079910.
##  5 cycle 4  15600       150143321.
##  6 cycle 5  15600        85777593.
##  7 cycle 6  15600        41915654.
##  8 cycle 7  15600        11007600.
##  9 cycle 8  15600         2208442.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251225742.
##  3 cycle 2  15600       201625287.
##  4 cycle 3  15600       197819032.
##  5 cycle 4  15600       148807845.
##  6 cycle 5  15600        84797928.
##  7 cycle 6  15600        41503231.
##  8 cycle 7  15600        11201821.
##  9 cycle 8  15600         2297291.
## 10 cycle 9  15600          307533.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       205018696.
##  4 cycle 3  15600       201046306.
##  5 cycle 4  15600       150924133.
##  6 cycle 5  15600        86587303.
##  7 cycle 6  15600        41522844.
##  8 cycle 7  15600        11413650.
##  9 cycle 8  15600         2367981.
## 10 cycle 9  15600          301257.
## 11 cycle 10 15600           84529.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       202634366.
##  4 cycle 3  15600       197196147.
##  5 cycle 4  15600       148449541.
##  6 cycle 5  15600        85255040.
##  7 cycle 6  15600        41336376.
##  8 cycle 7  15600        11074304.
##  9 cycle 8  15600         2487591.
## 10 cycle 9  15600          317993.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       202006877.
##  4 cycle 3  15600       198464078.
##  5 cycle 4  15600       150692811.
##  6 cycle 5  15600        86400572.
##  7 cycle 6  15600        42945114.
##  8 cycle 7  15600        11810967.
##  9 cycle 8  15600         2555592.
## 10 cycle 9  15600          317993.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       202602259.
##  4 cycle 3  15600       198729662.
##  5 cycle 4  15600       149226545.
##  6 cycle 5  15600        85083020.
##  7 cycle 6  15600        41339851.
##  8 cycle 7  15600        11022233.
##  9 cycle 8  15600         2337887.
## 10 cycle 9  15600          280336.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       203458956.
##  4 cycle 3  15600       198157934.
##  5 cycle 4  15600       147120341.
##  6 cycle 5  15600        84783522.
##  7 cycle 6  15600        41480634.
##  8 cycle 7  15600        11290820.
##  9 cycle 8  15600         2434104.
## 10 cycle 9  15600          366111.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       204740434.
##  4 cycle 3  15600       198447566.
##  5 cycle 4  15600       148510604.
##  6 cycle 5  15600        85002154.
##  7 cycle 6  15600        41559847.
##  8 cycle 7  15600        11929232.
##  9 cycle 8  15600         2599244.
## 10 cycle 9  15600          357742.
## 11 cycle 10 15600           90566.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       201331737.
##  4 cycle 3  15600       197471881.
##  5 cycle 4  15600       148163751.
##  6 cycle 5  15600        86146993.
##  7 cycle 6  15600        42226524.
##  8 cycle 7  15600        11507530.
##  9 cycle 8  15600         2474255.
## 10 cycle 9  15600          307533.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       203137122.
##  4 cycle 3  15600       198090846.
##  5 cycle 4  15600       147374133.
##  6 cycle 5  15600        84387438.
##  7 cycle 6  15600        41803183.
##  8 cycle 7  15600        11548963.
##  9 cycle 8  15600         2399698.
## 10 cycle 9  15600          259415.
## 11 cycle 10 15600           54340.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       201939350.
##  4 cycle 3  15600       198969745.
##  5 cycle 4  15600       147645127.
##  6 cycle 5  15600        84631043.
##  7 cycle 6  15600        41391989.
##  8 cycle 7  15600        11508926.
##  9 cycle 8  15600         2420768.
## 10 cycle 9  15600          324269.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       200690107.
##  4 cycle 3  15600       197882782.
##  5 cycle 4  15600       147403167.
##  6 cycle 5  15600        84885649.
##  7 cycle 6  15600        41628124.
##  8 cycle 7  15600        11183387.
##  9 cycle 8  15600         2298436.
## 10 cycle 9  15600          278244.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252090032.
##  3 cycle 2  15600       202988054.
##  4 cycle 3  15600       198760520.
##  5 cycle 4  15600       148936804.
##  6 cycle 5  15600        86287832.
##  7 cycle 6  15600        41925591.
##  8 cycle 7  15600        11856129.
##  9 cycle 8  15600         2434804.
## 10 cycle 9  15600          326361.
## 11 cycle 10 15600           84529.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       204074469.
##  4 cycle 3  15600       198911209.
##  5 cycle 4  15600       149079015.
##  6 cycle 5  15600        85239283.
##  7 cycle 6  15600        42308221.
##  8 cycle 7  15600        11407128.
##  9 cycle 8  15600         2599833.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       203292561.
##  4 cycle 3  15600       198890633.
##  5 cycle 4  15600       150316429.
##  6 cycle 5  15600        86458482.
##  7 cycle 6  15600        42325602.
##  8 cycle 7  15600        11631950.
##  9 cycle 8  15600         2307793.
## 10 cycle 9  15600          326361.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251807476.
##  3 cycle 2  15600       204019173.
##  4 cycle 3  15600       197803814.
##  5 cycle 4  15600       149759295.
##  6 cycle 5  15600        85459618.
##  7 cycle 6  15600        41608763.
##  8 cycle 7  15600        11660961.
##  9 cycle 8  15600         2300091.
## 10 cycle 9  15600          297073.
## 11 cycle 10 15600           84529.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       203990124.
##  4 cycle 3  15600       198530005.
##  5 cycle 4  15600       149122670.
##  6 cycle 5  15600        86101416.
##  7 cycle 6  15600        43147226.
##  8 cycle 7  15600        12062578.
##  9 cycle 8  15600         2547746.
## 10 cycle 9  15600          297073.
## 11 cycle 10 15600           54340.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252422451.
##  3 cycle 2  15600       201855643.
##  4 cycle 3  15600       197892483.
##  5 cycle 4  15600       148330233.
##  6 cycle 5  15600        86150092.
##  7 cycle 6  15600        43216753.
##  8 cycle 7  15600        11947665.
##  9 cycle 8  15600         2496471.
## 10 cycle 9  15600          303349.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       201763908.
##  4 cycle 3  15600       197430727.
##  5 cycle 4  15600       147305075.
##  6 cycle 5  15600        86200092.
##  7 cycle 6  15600        42437583.
##  8 cycle 7  15600        11482503.
##  9 cycle 8  15600         2366135.
## 10 cycle 9  15600          299165.
## 11 cycle 10 15600           60378.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252173137.
##  3 cycle 2  15600       203762572.
##  4 cycle 3  15600       197626622.
##  5 cycle 4  15600       149053612.
##  6 cycle 5  15600        86632547.
##  7 cycle 6  15600        42605932.
##  8 cycle 7  15600        11415872.
##  9 cycle 8  15600         2438894.
## 10 cycle 9  15600          338914.
## 11 cycle 10 15600           48302.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       203003214.
##  4 cycle 3  15600       198447566.
##  5 cycle 4  15600       147089650.
##  6 cycle 5  15600        84839036.
##  7 cycle 6  15600        41350274.
##  8 cycle 7  15600        11183448.
##  9 cycle 8  15600         2518241.
## 10 cycle 9  15600          311717.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       204158176.
##  4 cycle 3  15600       198369905.
##  5 cycle 4  15600       147027248.
##  6 cycle 5  15600        83329257.
##  7 cycle 6  15600        40593952.
##  8 cycle 7  15600        11329715.
##  9 cycle 8  15600         2498238.
## 10 cycle 9  15600          336822.
## 11 cycle 10 15600           93585.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       203632488.
##  4 cycle 3  15600       199704041.
##  5 cycle 4  15600       150764019.
##  6 cycle 5  15600        85730323.
##  7 cycle 6  15600        42577628.
##  8 cycle 7  15600        11551051.
##  9 cycle 8  15600         2586831.
## 10 cycle 9  15600          326361.
## 11 cycle 10 15600           54340.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       203130241.
##  4 cycle 3  15600       199472364.
##  5 cycle 4  15600       149273688.
##  6 cycle 5  15600        85268400.
##  7 cycle 6  15600        41959358.
##  8 cycle 7  15600        11521847.
##  9 cycle 8  15600         2556659.
## 10 cycle 9  15600          334730.
## 11 cycle 10 15600           51321.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       202494725.
##  4 cycle 3  15600       199336457.
##  5 cycle 4  15600       149101424.
##  6 cycle 5  15600        83995416.
##  7 cycle 6  15600        41351273.
##  8 cycle 7  15600        11613322.
##  9 cycle 8  15600         2400287.
## 10 cycle 9  15600          284520.
## 11 cycle 10 15600           51321.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       202591684.
##  4 cycle 3  15600       198775448.
##  5 cycle 4  15600       148004276.
##  6 cycle 5  15600        84111570.
##  7 cycle 6  15600        41090804.
##  8 cycle 7  15600        11054983.
##  9 cycle 8  15600         2265796.
## 10 cycle 9  15600          299165.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       201970566.
##  4 cycle 3  15600       196996491.
##  5 cycle 4  15600       151207628.
##  6 cycle 5  15600        87097199.
##  7 cycle 6  15600        43109485.
##  8 cycle 7  15600        11899650.
##  9 cycle 8  15600         2463243.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       201679054.
##  4 cycle 3  15600       198192700.
##  5 cycle 4  15600       147200037.
##  6 cycle 5  15600        85777269.
##  7 cycle 6  15600        41880902.
##  8 cycle 7  15600        11920683.
##  9 cycle 8  15600         2429759.
## 10 cycle 9  15600          297073.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       202111225.
##  4 cycle 3  15600       199066096.
##  5 cycle 4  15600       149886597.
##  6 cycle 5  15600        86891955.
##  7 cycle 6  15600        42078792.
##  8 cycle 7  15600        11385404.
##  9 cycle 8  15600         2398265.
## 10 cycle 9  15600          341006.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       202921545.
##  4 cycle 3  15600       198368453.
##  5 cycle 4  15600       150491337.
##  6 cycle 5  15600        85758413.
##  7 cycle 6  15600        42399347.
##  8 cycle 7  15600        11413212.
##  9 cycle 8  15600         2431414.
## 10 cycle 9  15600          286612.
## 11 cycle 10 15600           45283.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       201155147.
##  4 cycle 3  15600       195998644.
##  5 cycle 4  15600       147869679.
##  6 cycle 5  15600        84637224.
##  7 cycle 6  15600        41219427.
##  8 cycle 7  15600        11329022.
##  9 cycle 8  15600         2159888.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251873959.
##  3 cycle 2  15600       204612517.
##  4 cycle 3  15600       200046124.
##  5 cycle 4  15600       148821797.
##  6 cycle 5  15600        86200805.
##  7 cycle 6  15600        41800451.
##  8 cycle 7  15600        11653359.
##  9 cycle 8  15600         2425558.
## 10 cycle 9  15600          315901.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       203823091.
##  4 cycle 3  15600       196920572.
##  5 cycle 4  15600       145887289.
##  6 cycle 5  15600        84285311.
##  7 cycle 6  15600        41725955.
##  8 cycle 7  15600        11230710.
##  9 cycle 8  15600         2370415.
## 10 cycle 9  15600          338914.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       203728170.
##  4 cycle 3  15600       199851549.
##  5 cycle 4  15600       149890373.
##  6 cycle 5  15600        86424536.
##  7 cycle 6  15600        41800690.
##  8 cycle 7  15600        11493152.
##  9 cycle 8  15600         2387762.
## 10 cycle 9  15600          320085.
## 11 cycle 10 15600           87548.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       203011752.
##  4 cycle 3  15600       199762867.
##  5 cycle 4  15600       150097022.
##  6 cycle 5  15600        85282121.
##  7 cycle 6  15600        42937166.
##  8 cycle 7  15600        11550675.
##  9 cycle 8  15600         2395353.
## 10 cycle 9  15600          303349.
## 11 cycle 10 15600           84529.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       201830417.
##  4 cycle 3  15600       197429711.
##  5 cycle 4  15600       148741956.
##  6 cycle 5  15600        84898334.
##  7 cycle 6  15600        42390904.
##  8 cycle 7  15600        11576200.
##  9 cycle 8  15600         2426481.
## 10 cycle 9  15600          301257.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252056790.
##  3 cycle 2  15600       204043381.
##  4 cycle 3  15600       198949895.
##  5 cycle 4  15600       148502641.
##  6 cycle 5  15600        85442140.
##  7 cycle 6  15600        42477065.
##  8 cycle 7  15600        11185925.
##  9 cycle 8  15600         2452930.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           69434.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       202750180.
##  4 cycle 3  15600       196910726.
##  5 cycle 4  15600       145298301.
##  6 cycle 5  15600        84608449.
##  7 cycle 6  15600        41764200.
##  8 cycle 7  15600        11754780.
##  9 cycle 8  15600         2391708.
## 10 cycle 9  15600          374479.
## 11 cycle 10 15600           87548.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       204961234.
##  4 cycle 3  15600       200277087.
##  5 cycle 4  15600       148274108.
##  6 cycle 5  15600        85231391.
##  7 cycle 6  15600        42138630.
##  8 cycle 7  15600        11484603.
##  9 cycle 8  15600         2236403.
## 10 cycle 9  15600          317993.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252023548.
##  3 cycle 2  15600       203416402.
##  4 cycle 3  15600       199477433.
##  5 cycle 4  15600       149670009.
##  6 cycle 5  15600        86374527.
##  7 cycle 6  15600        42512822.
##  8 cycle 7  15600        11586971.
##  9 cycle 8  15600         2515775.
## 10 cycle 9  15600          343098.
## 11 cycle 10 15600          105661.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       202395091.
##  4 cycle 3  15600       198034052.
##  5 cycle 4  15600       148465961.
##  6 cycle 5  15600        86447509.
##  7 cycle 6  15600        42665775.
##  8 cycle 7  15600        11822176.
##  9 cycle 8  15600         2417012.
## 10 cycle 9  15600          290796.
## 11 cycle 10 15600           78491.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       250527662.
##  3 cycle 2  15600       199436402.
##  4 cycle 3  15600       196707440.
##  5 cycle 4  15600       146742158.
##  6 cycle 5  15600        84706792.
##  7 cycle 6  15600        42128207.
##  8 cycle 7  15600        11526473.
##  9 cycle 8  15600         2488769.
## 10 cycle 9  15600          274060.
## 11 cycle 10 15600           78491.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       201897178.
##  4 cycle 3  15600       197768323.
##  5 cycle 4  15600       147816898.
##  6 cycle 5  15600        84863036.
##  7 cycle 6  15600        41408379.
##  8 cycle 7  15600        11395848.
##  9 cycle 8  15600         2241225.
## 10 cycle 9  15600          303349.
## 11 cycle 10 15600           57359.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       204197801.
##  4 cycle 3  15600       199938330.
##  5 cycle 4  15600       151186207.
##  6 cycle 5  15600        86635285.
##  7 cycle 6  15600        42456705.
##  8 cycle 7  15600        11360000.
##  9 cycle 8  15600         2257473.
## 10 cycle 9  15600          330546.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251308847.
##  3 cycle 2  15600       202213279.
##  4 cycle 3  15600       198421776.
##  5 cycle 4  15600       147779549.
##  6 cycle 5  15600        84532024.
##  7 cycle 6  15600        42068612.
##  8 cycle 7  15600        11542502.
##  9 cycle 8  15600         2355902.
## 10 cycle 9  15600          290796.
## 11 cycle 10 15600           42264.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252239620.
##  3 cycle 2  15600       204212580.
##  4 cycle 3  15600       199297627.
##  5 cycle 4  15600       149741681.
##  6 cycle 5  15600        85688845.
##  7 cycle 6  15600        42578366.
##  8 cycle 7  15600        11538823.
##  9 cycle 8  15600         2388240.
## 10 cycle 9  15600          265692.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       201999997.
##  4 cycle 3  15600       198451038.
##  5 cycle 4  15600       149253285.
##  6 cycle 5  15600        85197147.
##  7 cycle 6  15600        41530050.
##  8 cycle 7  15600        11293612.
##  9 cycle 8  15600         2499383.
## 10 cycle 9  15600          313809.
## 11 cycle 10 15600           96604.
## 12 cycle 11 15600               0 
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       203026912.
##  4 cycle 3  15600       197975225.
##  5 cycle 4  15600       148656828.
##  6 cycle 5  15600        85860189.
##  7 cycle 6  15600        42359869.
##  8 cycle 7  15600        11329654.
##  9 cycle 8  15600         2543657.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       203277143.
##  4 cycle 3  15600       198474082.
##  5 cycle 4  15600       148150787.
##  6 cycle 5  15600        85148120.
##  7 cycle 6  15600        41907711.
##  8 cycle 7  15600        11757938.
##  9 cycle 8  15600         2477056.
## 10 cycle 9  15600          341006.
## 11 cycle 10 15600          108680.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       204003884.
##  4 cycle 3  15600       198405687.
##  5 cycle 4  15600       149075702.
##  6 cycle 5  15600        85297526.
##  7 cycle 6  15600        41254184.
##  8 cycle 7  15600        11139805.
##  9 cycle 8  15600         2230801.
## 10 cycle 9  15600          274060.
## 11 cycle 10 15600           57359.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251923822.
##  3 cycle 2  15600       202564419.
##  4 cycle 3  15600       198818766.
##  5 cycle 4  15600       148351798.
##  6 cycle 5  15600        85474348.
##  7 cycle 6  15600        41828008.
##  8 cycle 7  15600        11545223.
##  9 cycle 8  15600         2221922.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       201360915.
##  4 cycle 3  15600       196969249.
##  5 cycle 4  15600       148487239.
##  6 cycle 5  15600        86266598.
##  7 cycle 6  15600        43612547.
##  8 cycle 7  15600        11926766.
##  9 cycle 8  15600         2494593.
## 10 cycle 9  15600          309625.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       200682079.
##  4 cycle 3  15600       196143091.
##  5 cycle 4  15600       148685050.
##  6 cycle 5  15600        85974307.
##  7 cycle 6  15600        41876185.
##  8 cycle 7  15600        11379636.
##  9 cycle 8  15600         2336853.
## 10 cycle 9  15600          271968.
## 11 cycle 10 15600           66415.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       202917341.
##  4 cycle 3  15600       199601896.
##  5 cycle 4  15600       150692986.
##  6 cycle 5  15600        87886017.
##  7 cycle 6  15600        43547241.
##  8 cycle 7  15600        11903767.
##  9 cycle 8  15600         2571284.
## 10 cycle 9  15600          307533.
## 11 cycle 10 15600           48302.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       203397418.
##  4 cycle 3  15600       198340195.
##  5 cycle 4  15600       150624453.
##  6 cycle 5  15600        86740845.
##  7 cycle 6  15600        42530949.
##  8 cycle 7  15600        11996129.
##  9 cycle 8  15600         2602745.
## 10 cycle 9  15600          366111.
## 11 cycle 10 15600           78491.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       202427836.
##  4 cycle 3  15600       198374974.
##  5 cycle 4  15600       148487527.
##  6 cycle 5  15600        85465771.
##  7 cycle 6  15600        41692183.
##  8 cycle 7  15600        11465089.
##  9 cycle 8  15600         2470866.
## 10 cycle 9  15600          330546.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       203355117.
##  4 cycle 3  15600       197846565.
##  5 cycle 4  15600       147961783.
##  6 cycle 5  15600        85452087.
##  7 cycle 6  15600        41512921.
##  8 cycle 7  15600        11082354.
##  9 cycle 8  15600         2303481.
## 10 cycle 9  15600          322177.
## 11 cycle 10 15600           72453.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251358710.
##  3 cycle 2  15600       201953620.
##  4 cycle 3  15600       197559533.
##  5 cycle 4  15600       148896287.
##  6 cycle 5  15600        86451617.
##  7 cycle 6  15600        42518038.
##  8 cycle 7  15600        11785309.
##  9 cycle 8  15600         2485491.
## 10 cycle 9  15600          278244.
## 11 cycle 10 15600           75472.
## 12 cycle 11 15600           22418.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       203144383.
##  4 cycle 3  15600       198751545.
##  5 cycle 4  15600       147774405.
##  6 cycle 5  15600        84965478.
##  7 cycle 6  15600        41874686.
##  8 cycle 7  15600        11519758.
##  9 cycle 8  15600         2380951.
## 10 cycle 9  15600          347282.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600            2802.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       204267366.
##  4 cycle 3  15600       198671997.
##  5 cycle 4  15600       149495245.
##  6 cycle 5  15600        86141173.
##  7 cycle 6  15600        42623317.
##  8 cycle 7  15600        12220757.
##  9 cycle 8  15600         2631995.
## 10 cycle 9  15600          345190.
## 11 cycle 10 15600           63397.
## 12 cycle 11 15600            5605.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       203248222.
##  4 cycle 3  15600       198878609.
##  5 cycle 4  15600       149863664.
##  6 cycle 5  15600        84767774.
##  7 cycle 6  15600        42114296.
##  8 cycle 7  15600        11307735.
##  9 cycle 8  15600         2364002.
## 10 cycle 9  15600          290796.
## 11 cycle 10 15600           36227.
## 12 cycle 11 15600               0 
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       253421712.
##  2 cycle 1  15600       252206378.
##  3 cycle 2  15600       201879850.
##  4 cycle 3  15600       199360360.
##  5 cycle 4  15600       151597118.
##  6 cycle 5  15600        87080064.
##  7 cycle 6  15600        42281407.
##  8 cycle 7  15600        11491889.
##  9 cycle 8  15600         2490503.
## 10 cycle 9  15600          297073.
## 11 cycle 10 15600           84529.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0
# Females
discounted_costs_f <-
  map(final_cost_f2, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_f
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       140986640.
##  4 cycle 3  10400       118933208.
##  5 cycle 4  10400       116689861.
##  6 cycle 5  10400        82421730.
##  7 cycle 6  10400        58579413.
##  8 cycle 7  10400        22461839.
##  9 cycle 8  10400         4674456.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       139919022.
##  4 cycle 3  10400       117881176.
##  5 cycle 4  10400       115950953.
##  6 cycle 5  10400        82250165.
##  7 cycle 6  10400        57580598.
##  8 cycle 7  10400        22825508.
##  9 cycle 8  10400         4980252.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       141718940.
##  4 cycle 3  10400       119090258.
##  5 cycle 4  10400       116089225.
##  6 cycle 5  10400        82698618.
##  7 cycle 6  10400        57981475.
##  8 cycle 7  10400        22710235.
##  9 cycle 8  10400         5103376.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       141497447.
##  4 cycle 3  10400       119008367.
##  5 cycle 4  10400       116374913.
##  6 cycle 5  10400        82515190.
##  7 cycle 6  10400        58451316.
##  8 cycle 7  10400        22079378.
##  9 cycle 8  10400         4580984.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400           49990.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       140849255.
##  4 cycle 3  10400       119011277.
##  5 cycle 4  10400       115821401.
##  6 cycle 5  10400        82395698.
##  7 cycle 6  10400        56574287.
##  8 cycle 7  10400        22189636.
##  9 cycle 8  10400         5024446.
## 10 cycle 9  10400          365675.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       141911184.
##  4 cycle 3  10400       119133935.
##  5 cycle 4  10400       117113127.
##  6 cycle 5  10400        83194497.
##  7 cycle 6  10400        57602992.
##  8 cycle 7  10400        22367868.
##  9 cycle 8  10400         4460081.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       141947545.
##  4 cycle 3  10400       119096445.
##  5 cycle 4  10400       116455657.
##  6 cycle 5  10400        83172180.
##  7 cycle 6  10400        57760609.
##  8 cycle 7  10400        22247272.
##  9 cycle 8  10400         4858068.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220636028.
##  3 cycle 2  10400       142592100.
##  4 cycle 3  10400       119856762.
##  5 cycle 4  10400       117320641.
##  6 cycle 5  10400        83000606.
##  7 cycle 6  10400        58309918.
##  8 cycle 7  10400        22798569.
##  9 cycle 8  10400         5023403.
## 10 cycle 9  10400          327183.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       141783442.
##  4 cycle 3  10400       118650957.
##  5 cycle 4  10400       117356747.
##  6 cycle 5  10400        84082108.
##  7 cycle 6  10400        58419830.
##  8 cycle 7  10400        22734355.
##  9 cycle 8  10400         4914043.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221411398.
##  3 cycle 2  10400       142767271.
##  4 cycle 3  10400       119364141.
##  5 cycle 4  10400       118314377.
##  6 cycle 5  10400        84176949.
##  7 cycle 6  10400        59340496.
##  8 cycle 7  10400        23563493.
##  9 cycle 8  10400         5085505.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       141857905.
##  4 cycle 3  10400       117686819.
##  5 cycle 4  10400       116321344.
##  6 cycle 5  10400        82747444.
##  7 cycle 6  10400        57469181.
##  8 cycle 7  10400        22247899.
##  9 cycle 8  10400         4620938.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       141032962.
##  4 cycle 3  10400       118692448.
##  5 cycle 4  10400       116801655.
##  6 cycle 5  10400        82420106.
##  7 cycle 6  10400        57994131.
##  8 cycle 7  10400        23237411.
##  9 cycle 8  10400         5164065.
## 10 cycle 9  10400          346429.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       141600841.
##  4 cycle 3  10400       118837485.
##  5 cycle 4  10400       116239906.
##  6 cycle 5  10400        82096735.
##  7 cycle 6  10400        57311657.
##  8 cycle 7  10400        22048055.
##  9 cycle 8  10400         4880350.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220463723.
##  3 cycle 2  10400       140889253.
##  4 cycle 3  10400       118871698.
##  5 cycle 4  10400       116472319.
##  6 cycle 5  10400        82187171.
##  7 cycle 6  10400        58343924.
##  8 cycle 7  10400        22603422.
##  9 cycle 8  10400         5407826.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       141977582.
##  4 cycle 3  10400       119332838.
##  5 cycle 4  10400       117179777.
##  6 cycle 5  10400        82301314.
##  7 cycle 6  10400        58260062.
##  8 cycle 7  10400        22696452.
##  9 cycle 8  10400         5051205.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       141285599.
##  4 cycle 3  10400       119548850.
##  5 cycle 4  10400       117072344.
##  6 cycle 5  10400        83822197.
##  7 cycle 6  10400        57652940.
##  8 cycle 7  10400        23039133.
##  9 cycle 8  10400         5234951.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       141532701.
##  4 cycle 3  10400       118931205.
##  5 cycle 4  10400       116115515.
##  6 cycle 5  10400        83073605.
##  7 cycle 6  10400        59058715.
##  8 cycle 7  10400        22926371.
##  9 cycle 8  10400         5399345.
## 10 cycle 9  10400          373373.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       140553934.
##  4 cycle 3  10400       119104635.
##  5 cycle 4  10400       117045275.
##  6 cycle 5  10400        82766037.
##  7 cycle 6  10400        57145991.
##  8 cycle 7  10400        22204357.
##  9 cycle 8  10400         4764730.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220722180.
##  3 cycle 2  10400       143541937.
##  4 cycle 3  10400       119632015.
##  5 cycle 4  10400       117204677.
##  6 cycle 5  10400        82350364.
##  7 cycle 6  10400        58156141.
##  8 cycle 7  10400        22600289.
##  9 cycle 8  10400         4970625.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       141562267.
##  4 cycle 3  10400       120114448.
##  5 cycle 4  10400       116844502.
##  6 cycle 5  10400        82852291.
##  7 cycle 6  10400        58207779.
##  8 cycle 7  10400        22251657.
##  9 cycle 8  10400         4814681.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220743718.
##  3 cycle 2  10400       140596618.
##  4 cycle 3  10400       119433474.
##  5 cycle 4  10400       116928026.
##  6 cycle 5  10400        82670720.
##  7 cycle 6  10400        58785196.
##  8 cycle 7  10400        23107419.
##  9 cycle 8  10400         5090115.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       141584874.
##  4 cycle 3  10400       119633835.
##  5 cycle 4  10400       116856404.
##  6 cycle 5  10400        83071748.
##  7 cycle 6  10400        58854160.
##  8 cycle 7  10400        23008436.
##  9 cycle 8  10400         5069920.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400               0 
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       141449702.
##  4 cycle 3  10400       119388706.
##  5 cycle 4  10400       115466977.
##  6 cycle 5  10400        81546683.
##  7 cycle 6  10400        56773037.
##  8 cycle 7  10400        22600916.
##  9 cycle 8  10400         4903542.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       141670087.
##  4 cycle 3  10400       118017661.
##  5 cycle 4  10400       116096472.
##  6 cycle 5  10400        82156472.
##  7 cycle 6  10400        57751424.
##  8 cycle 7  10400        22556436.
##  9 cycle 8  10400         4782402.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           61099.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       140789180.
##  4 cycle 3  10400       119347215.
##  5 cycle 4  10400       116240306.
##  6 cycle 5  10400        82130206.
##  7 cycle 6  10400        58607336.
##  8 cycle 7  10400        23254954.
##  9 cycle 8  10400         4992167.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       140177982.
##  4 cycle 3  10400       118761419.
##  5 cycle 4  10400       116893310.
##  6 cycle 5  10400        82555643.
##  7 cycle 6  10400        58025894.
##  8 cycle 7  10400        22729030.
##  9 cycle 8  10400         5245925.
## 10 cycle 9  10400          361825.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       141124026.
##  4 cycle 3  10400       118884256.
##  5 cycle 4  10400       116351004.
##  6 cycle 5  10400        82040471.
##  7 cycle 6  10400        58275330.
##  8 cycle 7  10400        22668575.
##  9 cycle 8  10400         5054269.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       142193541.
##  4 cycle 3  10400       119753761.
##  5 cycle 4  10400       117745192.
##  6 cycle 5  10400        82684440.
##  7 cycle 6  10400        58336060.
##  8 cycle 7  10400        23160670.
##  9 cycle 8  10400         5031380.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       140823644.
##  4 cycle 3  10400       119637657.
##  5 cycle 4  10400       117004303.
##  6 cycle 5  10400        82723035.
##  7 cycle 6  10400        58368376.
##  8 cycle 7  10400        22589641.
##  9 cycle 8  10400         4914280.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       141837511.
##  4 cycle 3  10400       117485368.
##  5 cycle 4  10400       116346054.
##  6 cycle 5  10400        82755107.
##  7 cycle 6  10400        58805532.
##  8 cycle 7  10400        22940149.
##  9 cycle 8  10400         5168238.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220528337.
##  3 cycle 2  10400       140580651.
##  4 cycle 3  10400       118980886.
##  5 cycle 4  10400       115439402.
##  6 cycle 5  10400        81561795.
##  7 cycle 6  10400        57231450.
##  8 cycle 7  10400        22337484.
##  9 cycle 8  10400         4949453.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       142346104.
##  4 cycle 3  10400       119309909.
##  5 cycle 4  10400       115773688.
##  6 cycle 5  10400        81845646.
##  7 cycle 6  10400        57533659.
##  8 cycle 7  10400        23040386.
##  9 cycle 8  10400         5156087.
## 10 cycle 9  10400          357976.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       140977786.
##  4 cycle 3  10400       118878614.
##  5 cycle 4  10400       116906897.
##  6 cycle 5  10400        83303526.
##  7 cycle 6  10400        58155957.
##  8 cycle 7  10400        23037256.
##  9 cycle 8  10400         5375043.
## 10 cycle 9  10400          342579.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220851408.
##  3 cycle 2  10400       140479154.
##  4 cycle 3  10400       118746313.
##  5 cycle 4  10400       116378979.
##  6 cycle 5  10400        82573312.
##  7 cycle 6  10400        58304666.
##  8 cycle 7  10400        22339676.
##  9 cycle 8  10400         4557119.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       141839407.
##  4 cycle 3  10400       120106075.
##  5 cycle 4  10400       116012147.
##  6 cycle 5  10400        82333413.
##  7 cycle 6  10400        56547840.
##  8 cycle 7  10400        21898642.
##  9 cycle 8  10400         4795093.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       140702542.
##  4 cycle 3  10400       118864237.
##  5 cycle 4  10400       116551778.
##  6 cycle 5  10400        81142864.
##  7 cycle 6  10400        56778566.
##  8 cycle 7  10400        21965047.
##  9 cycle 8  10400         4615957.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       141805260.
##  4 cycle 3  10400       120026186.
##  5 cycle 4  10400       116630836.
##  6 cycle 5  10400        82414291.
##  7 cycle 6  10400        58335875.
##  8 cycle 7  10400        23119323.
##  9 cycle 8  10400         5149827.
## 10 cycle 9  10400          384921.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       142042402.
##  4 cycle 3  10400       119824369.
##  5 cycle 4  10400       116918799.
##  6 cycle 5  10400        82806723.
##  7 cycle 6  10400        58091294.
##  8 cycle 7  10400        22422998.
##  9 cycle 8  10400         4827942.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       142209508.
##  4 cycle 3  10400       118789078.
##  5 cycle 4  10400       115013061.
##  6 cycle 5  10400        80742069.
##  7 cycle 6  10400        57188536.
##  8 cycle 7  10400        22202166.
##  9 cycle 8  10400         4614981.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400           72208.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       142841416.
##  4 cycle 3  10400       118885347.
##  5 cycle 4  10400       116864640.
##  6 cycle 5  10400        82984336.
##  7 cycle 6  10400        58213769.
##  8 cycle 7  10400        22890660.
##  9 cycle 8  10400         4807141.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400           66654.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       142103901.
##  4 cycle 3  10400       119787791.
##  5 cycle 4  10400       116656620.
##  6 cycle 5  10400        82592147.
##  7 cycle 6  10400        57989339.
##  8 cycle 7  10400        21921820.
##  9 cycle 8  10400         4825789.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       140966247.
##  4 cycle 3  10400       118321934.
##  5 cycle 4  10400       117042305.
##  6 cycle 5  10400        83196588.
##  7 cycle 6  10400        58754171.
##  8 cycle 7  10400        22832085.
##  9 cycle 8  10400         4964364.
## 10 cycle 9  10400          384921.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220399109.
##  3 cycle 2  10400       139004440.
##  4 cycle 3  10400       118632940.
##  5 cycle 4  10400       116642738.
##  6 cycle 5  10400        82500312.
##  7 cycle 6  10400        58234013.
##  8 cycle 7  10400        22618769.
##  9 cycle 8  10400         4967494.
## 10 cycle 9  10400          257897.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221454474.
##  3 cycle 2  10400       142407920.
##  4 cycle 3  10400       119359044.
##  5 cycle 4  10400       116535410.
##  6 cycle 5  10400        82695594.
##  7 cycle 6  10400        57875773.
##  8 cycle 7  10400        22865602.
##  9 cycle 8  10400         4744905.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          188852.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       140962609.
##  4 cycle 3  10400       119212549.
##  5 cycle 4  10400       116434528.
##  6 cycle 5  10400        81987464.
##  7 cycle 6  10400        57646857.
##  8 cycle 7  10400        22461839.
##  9 cycle 8  10400         5058812.
## 10 cycle 9  10400          357976.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       140995177.
##  4 cycle 3  10400       118602366.
##  5 cycle 4  10400       115553871.
##  6 cycle 5  10400        81643400.
##  7 cycle 6  10400        56622546.
##  8 cycle 7  10400        21932782.
##  9 cycle 8  10400         4874090.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       140358052.
##  4 cycle 3  10400       117954331.
##  5 cycle 4  10400       115993610.
##  6 cycle 5  10400        81821471.
##  7 cycle 6  10400        57004778.
##  8 cycle 7  10400        22210936.
##  9 cycle 8  10400         4705625.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       141284492.
##  4 cycle 3  10400       118215111.
##  5 cycle 4  10400       116170369.
##  6 cycle 5  10400        82751859.
##  7 cycle 6  10400        58330438.
##  8 cycle 7  10400        22320256.
##  9 cycle 8  10400         4897718.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       142839203.
##  4 cycle 3  10400       120149023.
##  5 cycle 4  10400       117077210.
##  6 cycle 5  10400        83445344.
##  7 cycle 6  10400        57979601.
##  8 cycle 7  10400        22688309.
##  9 cycle 8  10400         4732754.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       141036282.
##  4 cycle 3  10400       119186526.
##  5 cycle 4  10400       116936852.
##  6 cycle 5  10400        83374668.
##  7 cycle 6  10400        58616428.
##  8 cycle 7  10400        22820496.
##  9 cycle 8  10400         5059856.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       142382782.
##  4 cycle 3  10400       120198342.
##  5 cycle 4  10400       117115507.
##  6 cycle 5  10400        82266901.
##  7 cycle 6  10400        58037045.
##  8 cycle 7  10400        22821121.
##  9 cycle 8  10400         5157434.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       142813592.
##  4 cycle 3  10400       120304437.
##  5 cycle 4  10400       117440271.
##  6 cycle 5  10400        83425809.
##  7 cycle 6  10400        58886690.
##  8 cycle 7  10400        23042891.
##  9 cycle 8  10400         4949216.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220765256.
##  3 cycle 2  10400       141765734.
##  4 cycle 3  10400       118959593.
##  5 cycle 4  10400       115459435.
##  6 cycle 5  10400        81095448.
##  7 cycle 6  10400        56905527.
##  8 cycle 7  10400        22267007.
##  9 cycle 8  10400         4746252.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220420647.
##  3 cycle 2  10400       140337341.
##  4 cycle 3  10400       118470428.
##  5 cycle 4  10400       116363896.
##  6 cycle 5  10400        82957371.
##  7 cycle 6  10400        58512415.
##  8 cycle 7  10400        22713993.
##  9 cycle 8  10400         5214082.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       144362770.
##  4 cycle 3  10400       118939941.
##  5 cycle 4  10400       117310614.
##  6 cycle 5  10400        83389089.
##  7 cycle 6  10400        57907905.
##  8 cycle 7  10400        22401072.
##  9 cycle 8  10400         5155651.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       140826964.
##  4 cycle 3  10400       118043322.
##  5 cycle 4  10400       116991011.
##  6 cycle 5  10400        83605755.
##  7 cycle 6  10400        58072925.
##  8 cycle 7  10400        22930442.
##  9 cycle 8  10400         5290052.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       141400849.
##  4 cycle 3  10400       118940486.
##  5 cycle 4  10400       116636587.
##  6 cycle 5  10400        82558900.
##  7 cycle 6  10400        57504600.
##  8 cycle 7  10400        22301464.
##  9 cycle 8  10400         5095872.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           72184.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221368322.
##  3 cycle 2  10400       140645788.
##  4 cycle 3  10400       118194184.
##  5 cycle 4  10400       116853138.
##  6 cycle 5  10400        83630854.
##  7 cycle 6  10400        58951998.
##  8 cycle 7  10400        23217991.
##  9 cycle 8  10400         5313008.
## 10 cycle 9  10400          388770.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       140623180.
##  4 cycle 3  10400       118205285.
##  5 cycle 4  10400       116707809.
##  6 cycle 5  10400        82196467.
##  7 cycle 6  10400        56819698.
##  8 cycle 7  10400        22149856.
##  9 cycle 8  10400         5096982.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       141812372.
##  4 cycle 3  10400       119986332.
##  5 cycle 4  10400       117286705.
##  6 cycle 5  10400        82397565.
##  7 cycle 6  10400        57252155.
##  8 cycle 7  10400        22226286.
##  9 cycle 8  10400         4819832.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       141128926.
##  4 cycle 3  10400       119588521.
##  5 cycle 4  10400       116808585.
##  6 cycle 5  10400        83064551.
##  7 cycle 6  10400        58774968.
##  8 cycle 7  10400        23156599.
##  9 cycle 8  10400         5318831.
## 10 cycle 9  10400          357976.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       141557050.
##  4 cycle 3  10400       118368522.
##  5 cycle 4  10400       117702745.
##  6 cycle 5  10400        83383265.
##  7 cycle 6  10400        58053725.
##  8 cycle 7  10400        22350638.
##  9 cycle 8  10400         4814511.
## 10 cycle 9  10400          361825.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221540627.
##  3 cycle 2  10400       140076485.
##  4 cycle 3  10400       117880630.
##  5 cycle 4  10400       116206285.
##  6 cycle 5  10400        82656066.
##  7 cycle 6  10400        58320977.
##  8 cycle 7  10400        23013760.
##  9 cycle 8  10400         5496080.
## 10 cycle 9  10400          361825.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       141151850.
##  4 cycle 3  10400       119180885.
##  5 cycle 4  10400       117445622.
##  6 cycle 5  10400        83312831.
##  7 cycle 6  10400        58679863.
##  8 cycle 7  10400        22714308.
##  9 cycle 8  10400         5045618.
## 10 cycle 9  10400          381071.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       142025328.
##  4 cycle 3  10400       119888427.
##  5 cycle 4  10400       116807700.
##  6 cycle 5  10400        82362684.
##  7 cycle 6  10400        58997155.
##  8 cycle 7  10400        23280952.
##  9 cycle 8  10400         5292509.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       140556937.
##  4 cycle 3  10400       117895007.
##  5 cycle 4  10400       117169771.
##  6 cycle 5  10400        83037568.
##  7 cycle 6  10400        58376178.
##  8 cycle 7  10400        23458869.
##  9 cycle 8  10400         5341483.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       141794192.
##  4 cycle 3  10400       118638399.
##  5 cycle 4  10400       117510292.
##  6 cycle 5  10400        82470564.
##  7 cycle 6  10400        56979281.
##  8 cycle 7  10400        22661058.
##  9 cycle 8  10400         4644567.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       141405594.
##  4 cycle 3  10400       119803987.
##  5 cycle 4  10400       117090102.
##  6 cycle 5  10400        82716530.
##  7 cycle 6  10400        57829941.
##  8 cycle 7  10400        22649469.
##  9 cycle 8  10400         4897652.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       141804942.
##  4 cycle 3  10400       119482972.
##  5 cycle 4  10400       116789648.
##  6 cycle 5  10400        82598186.
##  7 cycle 6  10400        58613050.
##  8 cycle 7  10400        22522607.
##  9 cycle 8  10400         5068876.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400               0 
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       141081815.
##  4 cycle 3  10400       118969786.
##  5 cycle 4  10400       116417065.
##  6 cycle 5  10400        81640376.
##  7 cycle 6  10400        57038138.
##  8 cycle 7  10400        22306786.
##  9 cycle 8  10400         4878500.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       140826175.
##  4 cycle 3  10400       119154499.
##  5 cycle 4  10400       116702458.
##  6 cycle 5  10400        81225161.
##  7 cycle 6  10400        57986820.
##  8 cycle 7  10400        22972729.
##  9 cycle 8  10400         5302507.
## 10 cycle 9  10400          411865.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221389860.
##  3 cycle 2  10400       141688268.
##  4 cycle 3  10400       120385416.
##  5 cycle 4  10400       117121447.
##  6 cycle 5  10400        83470461.
##  7 cycle 6  10400        58351512.
##  8 cycle 7  10400        22540463.
##  9 cycle 8  10400         4937909.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       141668663.
##  4 cycle 3  10400       119731739.
##  5 cycle 4  10400       116519232.
##  6 cycle 5  10400        82562857.
##  7 cycle 6  10400        57709616.
##  8 cycle 7  10400        22777583.
##  9 cycle 8  10400         5364609.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       142104218.
##  4 cycle 3  10400       119221285.
##  5 cycle 4  10400       116475100.
##  6 cycle 5  10400        82017220.
##  7 cycle 6  10400        57673276.
##  8 cycle 7  10400        22728403.
##  9 cycle 8  10400         4809464.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       141765261.
##  4 cycle 3  10400       120200524.
##  5 cycle 4  10400       117271538.
##  6 cycle 5  10400        82670486.
##  7 cycle 6  10400        58777885.
##  8 cycle 7  10400        22984004.
##  9 cycle 8  10400         5153564.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       140458443.
##  4 cycle 3  10400       118947402.
##  5 cycle 4  10400       116078419.
##  6 cycle 5  10400        81776837.
##  7 cycle 6  10400        57006282.
##  8 cycle 7  10400        22552989.
##  9 cycle 8  10400         4991493.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       140664758.
##  4 cycle 3  10400       117854607.
##  5 cycle 4  10400       116582133.
##  6 cycle 5  10400        82244817.
##  7 cycle 6  10400        58707325.
##  8 cycle 7  10400        22987137.
##  9 cycle 8  10400         5042858.
## 10 cycle 9  10400          342579.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221196018.
##  3 cycle 2  10400       142459459.
##  4 cycle 3  10400       119667502.
##  5 cycle 4  10400       116936747.
##  6 cycle 5  10400        82635624.
##  7 cycle 6  10400        57693980.
##  8 cycle 7  10400        22303029.
##  9 cycle 8  10400         4926801.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       141827867.
##  4 cycle 3  10400       119452765.
##  5 cycle 4  10400       116776355.
##  6 cycle 5  10400        82849733.
##  7 cycle 6  10400        59233197.
##  8 cycle 7  10400        23046653.
##  9 cycle 8  10400         5006338.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       140958817.
##  4 cycle 3  10400       119438025.
##  5 cycle 4  10400       117759980.
##  6 cycle 5  10400        84245767.
##  7 cycle 6  10400        58260062.
##  8 cycle 7  10400        22375386.
##  9 cycle 8  10400         4815421.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       140282483.
##  4 cycle 3  10400       119166507.
##  5 cycle 4  10400       116588874.
##  6 cycle 5  10400        82662356.
##  7 cycle 6  10400        58042851.
##  8 cycle 7  10400        22775704.
##  9 cycle 8  10400         5362218.
## 10 cycle 9  10400          373373.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       141229316.
##  4 cycle 3  10400       119786517.
##  5 cycle 4  10400       116936157.
##  6 cycle 5  10400        82789754.
##  7 cycle 6  10400        58098144.
##  8 cycle 7  10400        22879070.
##  9 cycle 8  10400         4991863.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       142124929.
##  4 cycle 3  10400       119693708.
##  5 cycle 4  10400       117877524.
##  6 cycle 5  10400        83973070.
##  7 cycle 6  10400        58525440.
##  8 cycle 7  10400        23144068.
##  9 cycle 8  10400         5056725.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       141866441.
##  4 cycle 3  10400       119333929.
##  5 cycle 4  10400       116126911.
##  6 cycle 5  10400        81971662.
##  7 cycle 6  10400        57644124.
##  8 cycle 7  10400        22255104.
##  9 cycle 8  10400         5016402.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       141442589.
##  4 cycle 3  10400       118668427.
##  5 cycle 4  10400       117473702.
##  6 cycle 5  10400        82906688.
##  7 cycle 6  10400        58063371.
##  8 cycle 7  10400        22856831.
##  9 cycle 8  10400         5251882.
## 10 cycle 9  10400          365675.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       142006830.
##  4 cycle 3  10400       119474420.
##  5 cycle 4  10400       116263120.
##  6 cycle 5  10400        81459963.
##  7 cycle 6  10400        58304389.
##  8 cycle 7  10400        22984630.
##  9 cycle 8  10400         4961604.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       142592100.
##  4 cycle 3  10400       119323012.
##  5 cycle 4  10400       116558814.
##  6 cycle 5  10400        81921212.
##  7 cycle 6  10400        57306035.
##  8 cycle 7  10400        22332160.
##  9 cycle 8  10400         4863285.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400           66654.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       142833986.
##  4 cycle 3  10400       119041851.
##  5 cycle 4  10400       116261034.
##  6 cycle 5  10400        81918188.
##  7 cycle 6  10400        56674553.
##  8 cycle 7  10400        22525739.
##  9 cycle 8  10400         5340373.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220506799.
##  3 cycle 2  10400       140997391.
##  4 cycle 3  10400       120213444.
##  5 cycle 4  10400       117287505.
##  6 cycle 5  10400        82683740.
##  7 cycle 6  10400        58334370.
##  8 cycle 7  10400        23070144.
##  9 cycle 8  10400         5020272.
## 10 cycle 9  10400          354127.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       141069323.
##  4 cycle 3  10400       118944854.
##  5 cycle 4  10400       116832494.
##  6 cycle 5  10400        82558442.
##  7 cycle 6  10400        57354386.
##  8 cycle 7  10400        22490345.
##  9 cycle 8  10400         5154371.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       140312048.
##  4 cycle 3  10400       118688809.
##  5 cycle 4  10400       117023157.
##  6 cycle 5  10400        83084993.
##  7 cycle 6  10400        58041929.
##  8 cycle 7  10400        23039133.
##  9 cycle 8  10400         4997754.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       142255674.
##  4 cycle 3  10400       119346853.
##  5 cycle 4  10400       118090200.
##  6 cycle 5  10400        83411630.
##  7 cycle 6  10400        58653536.
##  8 cycle 7  10400        22967089.
##  9 cycle 8  10400         4924847.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       141131456.
##  4 cycle 3  10400       118931389.
##  5 cycle 4  10400       116669617.
##  6 cycle 5  10400        82754883.
##  7 cycle 6  10400        58371847.
##  8 cycle 7  10400        22414542.
##  9 cycle 8  10400         5101526.
## 10 cycle 9  10400          384921.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       141349311.
##  4 cycle 3  10400       119124833.
##  5 cycle 4  10400       116273631.
##  6 cycle 5  10400        82444057.
##  7 cycle 6  10400        56858773.
##  8 cycle 7  10400        21870450.
##  9 cycle 8  10400         4871263.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220657566.
##  3 cycle 2  10400       140808467.
##  4 cycle 3  10400       119203814.
##  5 cycle 4  10400       115883796.
##  6 cycle 5  10400        81749638.
##  7 cycle 6  10400        57234829.
##  8 cycle 7  10400        22183371.
##  9 cycle 8  10400         4654631.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       142444281.
##  4 cycle 3  10400       120025096.
##  5 cycle 4  10400       117260731.
##  6 cycle 5  10400        82630725.
##  7 cycle 6  10400        58961459.
##  8 cycle 7  10400        22712739.
##  9 cycle 8  10400         5224887.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       141108849.
##  4 cycle 3  10400       118079900.
##  5 cycle 4  10400       115903345.
##  6 cycle 5  10400        82584466.
##  7 cycle 6  10400        59297398.
##  8 cycle 7  10400        23164426.
##  9 cycle 8  10400         4840463.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       141610485.
##  4 cycle 3  10400       118622748.
##  5 cycle 4  10400       116627360.
##  6 cycle 5  10400        82031408.
##  7 cycle 6  10400        57686946.
##  8 cycle 7  10400        22756908.
##  9 cycle 8  10400         5025119.
## 10 cycle 9  10400          361825.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       142073074.
##  4 cycle 3  10400       120268766.
##  5 cycle 4  10400       116752046.
##  6 cycle 5  10400        81991421.
##  7 cycle 6  10400        57346891.
##  8 cycle 7  10400        22053692.
##  9 cycle 8  10400         5035050.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       160238564.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       141497447.
##  4 cycle 3  10400       118771974.
##  5 cycle 4  10400       117582020.
##  6 cycle 5  10400        83559488.
##  7 cycle 6  10400        59388478.
##  8 cycle 7  10400        23358633.
##  9 cycle 8  10400         5226907.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0

The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:

#Males
tot_discounted_costs_m <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m[[i]]$discounted_costs) 
tot_discounted_costs_m[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1193689422
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1195884486
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1195318533
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1200070373
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1199460059
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1197592137
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1200048437
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1198549582
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1199259894
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1200728150
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1200451221
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1202568794
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1196674386
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1193836420
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1200506145
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1194950936
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1196451062
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1194268537
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1191343471
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1197661524
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1196137731
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1204409341
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1198798311
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1193912912
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1191401543
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1209373057
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1195354001
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1193483489
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1199152854
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1191946995
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1196109190
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1199149853
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1201913540
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1197885089
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1203168011
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1201668648
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1190536869
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1200666086
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1200719671
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1193082461
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1204545740
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1194213595
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1200608966
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1195717727
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1194160936
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1198156386
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1194751816
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1194098885
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1194586595
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1191126739
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1199126380
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1198957873
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1200812165
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1198244722
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1200940982
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1198127699
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1194547116
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1199531925
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1193997077
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1192595005
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1201920380
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1199182880
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1196170922
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1193649902
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1200258596
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1194579212
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1199355425
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1199120519
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1189793437
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1201249591
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1191794947
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1201351625
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1200579114
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1195361241
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1198935338
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1191269476
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1199939899
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1201467372
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1197472176
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1188040968
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1192917100
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1203730672
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1193982668
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1201442187
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1195947218
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1197166950
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1196489621
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1197073526
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1196542670
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1196567717
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1192611522
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1204724978
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1202042295
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1196368274
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1194767053
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1196811654
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1196186843
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1201797556
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1197934303
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1202207698
#Females
tot_discounted_costs_f <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f[[i]]$discounted_costs) 
tot_discounted_costs_f[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 926504590
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 923259898
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 927136060
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 925696788
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 923545172
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 927226226
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 927431260
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 930252548
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 929566116
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 934712152
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 924560168
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 926926670
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 924878083
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 925992152
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 928414942
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 929324798
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 928429403
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 925430432
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 929877950
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 928631179
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 927970606
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 929379722
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 923723343
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 924503824
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 926977269
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 926021257
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 925815307
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 930856240
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 927463156
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 927212778
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 922255572
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 926983983
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 928661380
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 924878147
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 925126292
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 922346856
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 929258706
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 928770767
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 922332122
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 929104382
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 927692123
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 927629629
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 923656042
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 928701386
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 925385908
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 921639905
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 921468338
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 925802108
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 930368821
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 928626482
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 929749035
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 932711552
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 922638745
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 925676594
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 931090236
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 927332998
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 926107563
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 928981988
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 923106821
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 927225999
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 929452687
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 928243811
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 925946970
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 929110996
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 930685120
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 927662368
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 926219511
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 927784217
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 928602335
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 923785824
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 926102802
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 930639513
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 928183278
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 926665644
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 930514666
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 923367961
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 925690976
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 928514974
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 929997779
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 929393881
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 926524683
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 928420910
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 932039945
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 925854752
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 928481242
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 926806055
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 926607378
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 926345508
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 928857528
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 926030388
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 926739995
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 931348230
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 927244162
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 924293183
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 923013229
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 931134781
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 926583281
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 925648225
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 927624581
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 930828367
#Averaging total costs across simulations
TDC_m_baseline <- mean(unlist(tot_discounted_costs_m))
TDC_f_baseline <- mean(unlist(tot_discounted_costs_f))
#Final result
TDC_baseline <- TDC_m_baseline + TDC_f_baseline
TDC_baseline
## [1] 2124658594

Alternative scenario: A1

The alternative scenario considers a 1-year delay in the onset of APD thanks to AI-based early detection. Physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which consequently reduces the probability of transitioning to the severe stage (P(MPD→APD)).

The increase in P(MPD→MPD) is modelled through the following formula: \[ p^\prime = p^{\frac{60-x}{60}}\]

where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Consequently, the new probability of transitioning to the severe stage is P(MPD→APD) = 1 – p’ – P(MPD→D).

According to the initial hypothesis, x = 12 months and therefore \[ p^\prime=\ p^\frac{60-12}{60}=p^\frac{4}{5} \].

Transition probabilities will be changed accordingly:

library(dplyr)
library(ggplot2)
library(fastmap)
library(purrr)
library(tibble)
library(tidyr)
library(forcats)
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")

generate_transition_matrix_alt_old <- function(summary_df, summary_df2, age_classes, gender_name) {
  
  x <- matrix(NA, nrow = 4, ncol = 4)

  x[1, 1] <- 0
  f_prob1 <- f_prob %>% 
    filter(`Age class` == age_class, Gender == gender_name) %>% 
    summarise(f_prob = F) %>% 
    pull(f_prob)
  x[1, 2] <- 1 - f_prob1
  x[1, 3] <- 0
  x[1, 4] <- f_prob1
  
  
   numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  x[2, 1] <- 0
  
  x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((numerator_MPD_MPD/denominator_MPD)^(4/5))

  x[2, 4] <- numerator_MPD_D / denominator_MPD

  x[2, 2] <- (numerator_MPD_MPD/denominator_MPD)^(4/5)

  x[3, 1] <- 0
  x[3, 2] <- 0
  numerator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  
  denominator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  
  x[3, 4] <- numerator_APD_D / denominator_APD_D

  x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)

  x[4, 1] <- 0
  x[4, 2] <- 0
  x[4, 3] <- 0
  x[4, 4] <- 1

  return(x)
}

transition_matrices_alt_old <- list()

for (gender in genders) {
  for (age_class in age_classes) {
    matrix_name <- paste(gender, age_class, sep = "_")
    transition_matrices_alt_old[[matrix_name]] <- generate_transition_matrix_alt_old(summary_df, summary_df2, age_class, gender)
  }
}


transition_matrices_alt_old
## $`Male_50-54`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9712352 0.0000000 0.02876483
## [2,]    0 0.8717192 0.0782808 0.05000000
## [3,]    0 0.0000000 0.9291339 0.07086614
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Male_55-59`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9574518 0.00000000 0.04254822
## [2,]    0 0.8755513 0.05506091 0.06938776
## [3,]    0 0.0000000 0.87280702 0.12719298
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_60-64`
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9433756 0.0000000 0.05662437
## [2,]    0 0.8594377 0.0355623 0.10500000
## [3,]    0 0.0000000 0.8191489 0.18085106
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## $`Male_65-69`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9224868 0.00000000 0.07751319
## [2,]    0 0.7960183 0.02388092 0.18010076
## [3,]    0 0.0000000 0.69558600 0.30441400
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_70-74`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.8875735 0.000000000 0.1124265
## [2,]    0 0.7568883 0.005142376 0.2379693
## [3,]    0 0.0000000 0.570370370 0.4296296
## [4,]    0 0.0000000 0.000000000 1.0000000
## 
## $`Male_75-79`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.8201575  0.00000000 0.1798425
## [2,]    0 0.6857768 -0.01200958 0.3262327
## [3,]    0 0.0000000  0.48199768 0.5180023
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Male_80-84`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.7046099  0.00000000 0.2953901
## [2,]    0 0.5818083 -0.03967971 0.4578714
## [3,]    0 0.0000000  0.33866995 0.6613300
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Male_85-89`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.5279737  0.00000000 0.4720263
## [2,]    0 0.4347701 -0.06089622 0.6261261
## [3,]    0 0.0000000  0.25708502 0.7429150
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Male_90-94`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.3260733  0.00000000 0.6739267
## [2,]    0 0.3147570 -0.06792152 0.7531646
## [3,]    0 0.0000000  0.16030534 0.8396947
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Male_95et+`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.1585850  0.00000000 0.8414150
## [2,]    0 0.2205748 -0.06941202 0.8488372
## [3,]    0 0.0000000  0.11111111 0.8888889
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Female_50-54`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9864538 0.00000000 0.01354618
## [2,]    0 0.9226699 0.04762714 0.02970297
## [3,]    0 0.0000000 0.91935484 0.08064516
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_55-59`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9814785 0.00000000 0.01852146
## [2,]    0 0.9267586 0.02207857 0.05116279
## [3,]    0 0.0000000 0.86885246 0.13114754
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_60-64`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9750718 0.00000000 0.02492824
## [2,]    0 0.9126612 0.03904335 0.04829545
## [3,]    0 0.0000000 0.85654008 0.14345992
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_65-69`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9644648 0.00000000 0.03553525
## [2,]    0 0.8736398 0.01892216 0.10743802
## [3,]    0 0.0000000 0.77889447 0.22110553
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_70-74`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9455591 0.00000000 0.05444087
## [2,]    0 0.8303303 0.02067638 0.14899329
## [3,]    0 0.0000000 0.71125265 0.28874735
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_75-79`
##      [,1]      [,2]        [,3]       [,4]
## [1,]    0 0.9040836 0.000000000 0.09591642
## [2,]    0 0.7688932 0.001606841 0.22950000
## [3,]    0 0.0000000 0.618098160 0.38190184
## [4,]    0 0.0000000 0.000000000 1.00000000
## 
## $`Female_80-84`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.8160931  0.00000000 0.1839069
## [2,]    0 0.6921716 -0.02698224 0.3348106
## [3,]    0 0.0000000  0.48024316 0.5197568
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Female_85-89`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.6559712  0.00000000 0.3440288
## [2,]    0 0.5709296 -0.05580811 0.4848785
## [3,]    0 0.0000000  0.37564767 0.6243523
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Female_90-94`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.4385294  0.00000000 0.5614706
## [2,]    0 0.4163927 -0.07613896 0.6597463
## [3,]    0 0.0000000  0.26804124 0.7319588
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Female_95et+`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.2311448  0.0000000 0.7688552
## [2,]    0 0.3727036 -0.0760003 0.7032967
## [3,]    0 0.0000000  0.2222222 0.7777778
## [4,]    0 0.0000000  0.0000000 1.0000000
names(transition_matrices_alt_old) <- NULL  

males_alt_old <- transition_matrices_alt_old[1:10]
females_alt_old <- transition_matrices_alt_old[11:20]

matrices_mf_alt_old <- list(males_alt_old, females_alt_old)
matrices_mf_old
## [[1]]
## [[1]][[1]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9712352 0.0000000 0.02876483
## [2,]    0 0.8423077 0.1076923 0.05000000
## [3,]    0 0.0000000 0.9291339 0.07086614
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[2]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9574518 0.00000000 0.04254822
## [2,]    0 0.8469388 0.08367347 0.06938776
## [3,]    0 0.0000000 0.87280702 0.12719298
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[3]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9433756 0.0000000 0.05662437
## [2,]    0 0.8275000 0.0675000 0.10500000
## [3,]    0 0.0000000 0.8191489 0.18085106
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[4]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9224868 0.00000000 0.07751319
## [2,]    0 0.7518892 0.06801008 0.18010076
## [3,]    0 0.0000000 0.69558600 0.30441400
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[5]]
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.8875735 0.0000000 0.1124265
## [2,]    0 0.7059757 0.0560550 0.2379693
## [3,]    0 0.0000000 0.5703704 0.4296296
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## [[1]][[6]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8201575 0.00000000 0.1798425
## [2,]    0 0.6240631 0.04970414 0.3262327
## [3,]    0 0.0000000 0.48199768 0.5180023
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[7]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.7046099 0.00000000 0.2953901
## [2,]    0 0.5081301 0.03399852 0.4578714
## [3,]    0 0.0000000 0.33866995 0.6613300
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[8]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.5279737 0.00000000 0.4720263
## [2,]    0 0.3530405 0.02083333 0.6261261
## [3,]    0 0.0000000 0.25708502 0.7429150
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[9]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.3260733 0.00000000 0.6739267
## [2,]    0 0.2357595 0.01107595 0.7531646
## [3,]    0 0.0000000 0.16030534 0.8396947
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[1]][[10]]
##      [,1]      [,2]      [,3]      [,4]
## [1,]    0 0.1585850 0.0000000 0.8414150
## [2,]    0 0.1511628 0.0000000 0.8488372
## [3,]    0 0.0000000 0.1111111 0.8888889
## [4,]    0 0.0000000 0.0000000 1.0000000
## 
## 
## [[2]]
## [[2]][[1]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9864538 0.0000000 0.01354618
## [2,]    0 0.9042904 0.0660066 0.02970297
## [3,]    0 0.0000000 0.9193548 0.08064516
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[2]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9814785 0.00000000 0.01852146
## [2,]    0 0.9093023 0.03953488 0.05116279
## [3,]    0 0.0000000 0.86885246 0.13114754
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[3]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9750718 0.00000000 0.02492824
## [2,]    0 0.8920455 0.05965909 0.04829545
## [3,]    0 0.0000000 0.85654008 0.14345992
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[4]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9644648 0.00000000 0.03553525
## [2,]    0 0.8446281 0.04793388 0.10743802
## [3,]    0 0.0000000 0.77889447 0.22110553
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[5]]
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9455591 0.00000000 0.05444087
## [2,]    0 0.7926174 0.05838926 0.14899329
## [3,]    0 0.0000000 0.71125265 0.28874735
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[6]]
##      [,1]      [,2]      [,3]       [,4]
## [1,]    0 0.9040836 0.0000000 0.09591642
## [2,]    0 0.7200000 0.0505000 0.22950000
## [3,]    0 0.0000000 0.6180982 0.38190184
## [4,]    0 0.0000000 0.0000000 1.00000000
## 
## [[2]][[7]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8160931 0.00000000 0.1839069
## [2,]    0 0.6313457 0.03384367 0.3348106
## [3,]    0 0.0000000 0.48024316 0.5197568
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[8]]
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.6559712 0.00000000 0.3440288
## [2,]    0 0.4962816 0.01883986 0.4848785
## [3,]    0 0.0000000 0.37564767 0.6243523
## [4,]    0 0.0000000 0.00000000 1.0000000
## 
## [[2]][[9]]
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.4385294 0.000000000 0.5614706
## [2,]    0 0.3344867 0.005767013 0.6597463
## [3,]    0 0.0000000 0.268041237 0.7319588
## [4,]    0 0.0000000 0.000000000 1.0000000
## 
## [[2]][[10]]
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.2311448 0.000000000 0.7688552
## [2,]    0 0.2912088 0.005494505 0.7032967
## [3,]    0 0.0000000 0.222222222 0.7777778
## [4,]    0 0.0000000 0.000000000 1.0000000
for (i in 1:length(males_alt_old)) {
  colnames(males_alt_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
  rownames(males_alt_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
}
for (i in 1:length(females_alt_old)) {
  colnames(females_alt_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_alt_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males_alt_old)) {
  dimnames(males_alt_old[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females_alt_old)) {
  dimnames(females_alt_old[[i]]) <- list(row_names_f, col_names_f)
}

transition_matrices_mf_alt_old <- list(males_alt_old, females_alt_old)
transition_matrices_mf_alt_old
## [[1]]
## [[1]][[1]]
##       P.m     MPD.m     APD.m        D.m
## P.m     0 0.9712352 0.0000000 0.02876483
## MPD.m   0 0.8717192 0.0782808 0.05000000
## APD.m   0 0.0000000 0.9291339 0.07086614
## D.m     0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[2]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9574518 0.00000000 0.04254822
## MPD.m   0 0.8755513 0.05506091 0.06938776
## APD.m   0 0.0000000 0.87280702 0.12719298
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[3]]
##       P.m     MPD.m     APD.m        D.m
## P.m     0 0.9433756 0.0000000 0.05662437
## MPD.m   0 0.8594377 0.0355623 0.10500000
## APD.m   0 0.0000000 0.8191489 0.18085106
## D.m     0 0.0000000 0.0000000 1.00000000
## 
## [[1]][[4]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9224868 0.00000000 0.07751319
## MPD.m   0 0.7960183 0.02388092 0.18010076
## APD.m   0 0.0000000 0.69558600 0.30441400
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[5]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.8875735 0.000000000 0.1124265
## MPD.m   0 0.7568883 0.005142376 0.2379693
## APD.m   0 0.0000000 0.570370370 0.4296296
## D.m     0 0.0000000 0.000000000 1.0000000
## 
## [[1]][[6]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.8201575  0.00000000 0.1798425
## MPD.m   0 0.6857768 -0.01200958 0.3262327
## APD.m   0 0.0000000  0.48199768 0.5180023
## D.m     0 0.0000000  0.00000000 1.0000000
## 
## [[1]][[7]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.7046099  0.00000000 0.2953901
## MPD.m   0 0.5818083 -0.03967971 0.4578714
## APD.m   0 0.0000000  0.33866995 0.6613300
## D.m     0 0.0000000  0.00000000 1.0000000
## 
## [[1]][[8]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.5279737  0.00000000 0.4720263
## MPD.m   0 0.4347701 -0.06089622 0.6261261
## APD.m   0 0.0000000  0.25708502 0.7429150
## D.m     0 0.0000000  0.00000000 1.0000000
## 
## [[1]][[9]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.3260733  0.00000000 0.6739267
## MPD.m   0 0.3147570 -0.06792152 0.7531646
## APD.m   0 0.0000000  0.16030534 0.8396947
## D.m     0 0.0000000  0.00000000 1.0000000
## 
## [[1]][[10]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.1585850  0.00000000 0.8414150
## MPD.m   0 0.2205748 -0.06941202 0.8488372
## APD.m   0 0.0000000  0.11111111 0.8888889
## D.m     0 0.0000000  0.00000000 1.0000000
## 
## 
## [[2]]
## [[2]][[1]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9864538 0.00000000 0.01354618
## MPD.f   0 0.9226699 0.04762714 0.02970297
## APD.f   0 0.0000000 0.91935484 0.08064516
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[2]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9814785 0.00000000 0.01852146
## MPD.f   0 0.9267586 0.02207857 0.05116279
## APD.f   0 0.0000000 0.86885246 0.13114754
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[3]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9750718 0.00000000 0.02492824
## MPD.f   0 0.9126612 0.03904335 0.04829545
## APD.f   0 0.0000000 0.85654008 0.14345992
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[4]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9644648 0.00000000 0.03553525
## MPD.f   0 0.8736398 0.01892216 0.10743802
## APD.f   0 0.0000000 0.77889447 0.22110553
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[5]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9455591 0.00000000 0.05444087
## MPD.f   0 0.8303303 0.02067638 0.14899329
## APD.f   0 0.0000000 0.71125265 0.28874735
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[6]]
##       P.f     MPD.f       APD.f        D.f
## P.f     0 0.9040836 0.000000000 0.09591642
## MPD.f   0 0.7688932 0.001606841 0.22950000
## APD.f   0 0.0000000 0.618098160 0.38190184
## D.f     0 0.0000000 0.000000000 1.00000000
## 
## [[2]][[7]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.8160931  0.00000000 0.1839069
## MPD.f   0 0.6921716 -0.02698224 0.3348106
## APD.f   0 0.0000000  0.48024316 0.5197568
## D.f     0 0.0000000  0.00000000 1.0000000
## 
## [[2]][[8]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.6559712  0.00000000 0.3440288
## MPD.f   0 0.5709296 -0.05580811 0.4848785
## APD.f   0 0.0000000  0.37564767 0.6243523
## D.f     0 0.0000000  0.00000000 1.0000000
## 
## [[2]][[9]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.4385294  0.00000000 0.5614706
## MPD.f   0 0.4163927 -0.07613896 0.6597463
## APD.f   0 0.0000000  0.26804124 0.7319588
## D.f     0 0.0000000  0.00000000 1.0000000
## 
## [[2]][[10]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.2311448  0.0000000 0.7688552
## MPD.f   0 0.3727036 -0.0760003 0.7032967
## APD.f   0 0.0000000  0.2222222 0.7777778
## D.f     0 0.0000000  0.0000000 1.0000000
transition_matrices_m_alt_old <- transition_matrices_mf_alt_old[[1]]
transition_matrices_f_alt_old <- transition_matrices_mf_alt_old[[2]]

extract_rows_as_named_list <- function(matrix) {
  list(
    P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
    MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
    APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
    D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
  )
}

transition_prob_m_alt_old <- lapply(transition_matrices_m_alt_old, extract_rows_as_named_list)

transition_prob_f_alt_old <- lapply(transition_matrices_f_alt_old, extract_rows_as_named_list)

print("Transition Probabilities for Males:")
## [1] "Transition Probabilities for Males:"
print(transition_prob_m_alt_old)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8717192 0.0782808 0.0500000 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87555133 0.05506091 0.06938776 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8594377 0.0355623 0.1050000 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.79601832 0.02388092 0.18010076 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.756888296 0.005142376 0.237969328 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.68577684 -0.01200958  0.32623274 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.58180832 -0.03967971  0.45787140 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.43477009 -0.06089622  0.62612613 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.31475697 -0.06792152  0.75316456 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##        P      MPD      APD        D 
## 0.000000 0.158585 0.000000 0.841415 
## 
## [[10]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.22057481 -0.06941202  0.84883721 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Transition Probabilities for Females:")
## [1] "Transition Probabilities for Females:"
print(transition_prob_f_alt_old)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92266989 0.04762714 0.02970297 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92675864 0.02207857 0.05116279 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91266120 0.03904335 0.04829545 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87363982 0.01892216 0.10743802 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.83033033 0.02067638 0.14899329 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.768893159 0.001606841 0.229500000 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.69217160 -0.02698224  0.33481064 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.57092958 -0.05580811  0.48487853 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.41639271 -0.07613896  0.65974625 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2311448 0.0000000 0.7688552 
## 
## [[10]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.3727036 -0.0760003  0.7032967 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1

The graph showcasing probabilities of death with respect to severity:

severity_labels <- c("Prodromal", "Mild", "Advanced")

# Extracting probabilities of death from matrices
extract_probabilities <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[1],
      probability_of_death = matrix[1, 4]
    ))
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_death = matrix[2, 4]
    ))
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[3],
      probability_of_death = matrix[3, 4]
    ))
  }
  
  return(data)
}

# Extracting data for males/females
males_data_alt_old <- extract_probabilities(males_alt_old, age_classes, "Male")
females_data_alt_old <- extract_probabilities(females_alt_old, age_classes, "Female")

final_data_alt_old <- rbind(males_data_alt_old, females_data_alt_old)

# Let's apply the adjustment
final_data_alt1_old <- final_data_alt_old %>%
  group_by(gender) %>% 
  mutate(probability_of_death = ifelse(
    age_class == "95et+" & severity == "Prodromal",
    probability_of_death[age_class == "95et+" & severity == "Mild"] -
      (probability_of_death[age_class == "90-94" & severity == "Mild"] -
       probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
    probability_of_death
  ))

graph_prob_mf_alt <- ggplot(final_data_alt1_old, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
  theme_minimal() +
  labs(title = "Probability of death with respect to severity, alternative scenario",
       x = "Age class",
       y = "Probability",
       color = "Severity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf_alt

Considering the alternative scenario A1, the proposed assumption is to consider a 1-year delay in the onset of APD thanks to AI-based early detection. The manipulation of the prodromal period should not be considered as this stage cannot be precisely detected by definition, as well as the rigor of the criteria used to distinguish between MPD and APD should not be varied as such variation is already used to tackle the issue related to the unclear definition of APD. The new approach suggests that physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which proportionally reduces the probability of transitioning to the severe stage (P(MPD→APD)) and the probability of dying (P(MPD→D)). The increase in P(MPD→MPD) is modeled through the following formula:

\[ p^\prime=\ p^\frac{60-x}{60} \] where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Accordingly, the positive gain in P(MPD→APD) is defined as:

\[ \mathrm{\Delta}\ =\ p^\prime\ -\ p \] This gain is counterbalanced by a proportional redistribution of its negative value, - delta, among the other two transition probabilities having MPD as the initial state, namely P(MPD→APD) and P(MPD→D). For this purpose, the negative gain is decomposed into:

\[ -\ \mathrm{\Delta}\ =\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD})\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]

The two components are proportional to the initial probabilities computed in the baseline scenario:

\[ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{APD})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \] \[ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{D})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \]

Consequently, the new probabilities for the alternative scenario are defined as:

\[ p'(\mathrm{MPD} \rightarrow \mathrm{APD}) = p(\mathrm{MPD} \rightarrow \mathrm{APD}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) \]

\[ p'(\mathrm{MPD} \rightarrow \mathrm{D}) = p(\mathrm{MPD} \rightarrow \mathrm{D}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]

In this way, the sum to 1 for the second row of the transition matrices is ensured in the alternative scenario A1.

# Adjust probability_of_death for 95+ patients
final_data1_alt <- final_data_alt_old %>%
  group_by(gender) %>%
  mutate(probability_of_death = ifelse(
    age_class == "95et+" & severity == "Prodromal",
    probability_of_death[age_class == "95et+" & severity == "Mild"] -
      (probability_of_death[age_class == "90-94" & severity == "Mild"] -
       probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
    probability_of_death
  ))

age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")

# Update f_prob1 with correct probabilities
f_prob1 <- f_prob %>%
  mutate(
    F = case_when(
      `Age class` == "95et+" & Gender == "Male" ~ final_data1_alt %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      `Age class` == "95et+" & Gender == "Female" ~ final_data1_alt %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      TRUE ~ F
    )
  )

# Function to generate transition matrix
generate_transition_matrix_alt <- function(summary_df, summary_df2, final_data1_alt, age_class, gender_name) {
  x <- matrix(NA, nrow = 4, ncol = 4)
  x[1, 1] <- 0

  f_prob2 <- f_prob1 %>%
    filter(`Age class` == age_class & Gender == gender_name) %>%
    pull(F)
   
  x[1, 2] <- 1 - f_prob2
  x[1, 3] <- 0
  x[1, 4] <- f_prob2

  x[2, 1] <- 0

  numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
    x[2, 4] <- numerator_MPD_D / denominator_MPD
  } else {
    x[2, 4] <- NA
  }

  if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
    x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((numerator_MPD_MPD / denominator_MPD)^(4/5))
  } else {
    x[2, 3] <- NA
  }

  x[2, 2] <- ifelse(length(numerator_MPD_MPD) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0, 
                    (numerator_MPD_MPD / denominator_MPD)^(4/5), NA)

  x[3, 1] <- 0
  x[3, 2] <- 0
  numerator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)
  
  denominator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  if (length(numerator_APD_D) > 0 && length(denominator_APD_D) > 0 && denominator_APD_D != 0) {
    x[3, 4] <- numerator_APD_D / denominator_APD_D
    x[3, 3] <- 1 - x[3, 4]
  } else {
    x[3, 4] <- NA
    x[3, 3] <- NA
  }

  x[4, 1] <- 0
  x[4, 2] <- 0
  x[4, 3] <- 0
  x[4, 4] <- 1

  return(x)
}

transition_matrices_alt1 <- list()

for (gender in genders) {
  for (age_class in age_classes) {
    matrix_name <- paste(gender, age_class, sep = "_")
    transition_matrices_alt1[[matrix_name]] <- generate_transition_matrix_alt(summary_df, summary_df2, final_data1_alt, age_class, gender)
  }
}

names(transition_matrices_alt1) <- NULL  

males_alt1 <- transition_matrices_alt1[1:10]
females_alt1 <- transition_matrices_alt1[11:20]

matrices_mf_alt1 <- list(males_alt1, females_alt1)

for (i in 1:length(males_alt1)) {
  colnames(males_alt1[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  rownames(males_alt1[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt1)) {
  colnames(females_alt1[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_alt1[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}

transition_matrices_m_alt1 <- matrices_mf_alt1[[1]]
transition_matrices_f_alt1 <- matrices_mf_alt1[[2]]

extract_rows_as_named_list <- function(matrix) {
  list(
    P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
    MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
    APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
    D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
  )
}

transition_prob_m_alt1 <- lapply(transition_matrices_m_alt1, extract_rows_as_named_list)
transition_prob_f_alt1 <- lapply(transition_matrices_f_alt1, extract_rows_as_named_list)

print("Transition Probabilities for Males:")
## [1] "Transition Probabilities for Males:"
print(transition_prob_m_alt1)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8717192 0.0782808 0.0500000 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87555133 0.05506091 0.06938776 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8594377 0.0355623 0.1050000 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.79601832 0.02388092 0.18010076 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.756888296 0.005142376 0.237969328 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.68577684 -0.01200958  0.32623274 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.58180832 -0.03967971  0.45787140 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.43477009 -0.06089622  0.62612613 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.31475697 -0.06792152  0.75316456 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2304007 0.0000000 0.7695993 
## 
## [[10]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.22057481 -0.06941202  0.84883721 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Transition Probabilities for Females:")
## [1] "Transition Probabilities for Females:"
print(transition_prob_f_alt1)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92266989 0.04762714 0.02970297 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92675864 0.02207857 0.05116279 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91266120 0.03904335 0.04829545 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87363982 0.01892216 0.10743802 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.83033033 0.02067638 0.14899329 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.768893159 0.001606841 0.229500000 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.69217160 -0.02698224  0.33481064 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.57092958 -0.05580811  0.48487853 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.41639271 -0.07613896  0.65974625 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3949789 0.0000000 0.6050211 
## 
## [[10]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.3727036 -0.0760003  0.7032967 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
# Function to calculate delta
calculate_delta <- function(baseline, alt) {
  delta <- alt - baseline
  return(delta)
}

# Function to update transition probabilities based on delta distribution
update_transition_probabilities <- function(transition_prob_m, transition_prob_f, transition_prob_m_alt1, transition_prob_f_alt1) {
  for (i in 1:length(transition_prob_m)) {
    # Extract baseline and alternative matrices
    baseline_matrix_m <- transition_prob_m[[i]]$MPD
    alt_matrix_m <- transition_prob_m_alt1[[i]]$MPD
    baseline_matrix_f <- transition_prob_f[[i]]$MPD
    alt_matrix_f <- transition_prob_f_alt1[[i]]$MPD
    
    # Baseline and alternative [2,2] elements
    baseline_m_MPD <- baseline_matrix_m["MPD"]
    alt_m_MPD <- alt_matrix_m["MPD"]
    
    baseline_f_MPD <- baseline_matrix_f["MPD"]
    alt_f_MPD <- alt_matrix_f["MPD"]
    
    # Calculate deltas
    delta_m <- calculate_delta(baseline_m_MPD, alt_m_MPD)
    delta_f <- calculate_delta(baseline_f_MPD, alt_f_MPD)
    
    # Calculate baseline probabilities
    p_m_APD <- baseline_matrix_m["APD"]
    p_m_D <- baseline_matrix_m["D"]
    p_f_APD <- baseline_matrix_f["APD"]
    p_f_D <- baseline_matrix_f["D"]
    
    # Calculate delta distribution for males
    sum_m_APD_D <- p_m_APD + p_m_D
    delta_m_APD <- (p_m_APD / sum_m_APD_D) * delta_m
    delta_m_D <- (p_m_D / sum_m_APD_D) * delta_m
    
    # Calculate delta distribution for females
    sum_f_APD_D <- p_f_APD + p_f_D
    delta_f_APD <- (p_f_APD / sum_f_APD_D) * delta_f
    delta_f_D <- (p_f_D / sum_f_APD_D) * delta_f
    
    # Update alternative transition probabilities for males
    transition_prob_m_alt1[[i]]$MPD["APD"] <- baseline_matrix_m["APD"] - delta_m_APD
    transition_prob_m_alt1[[i]]$MPD["D"] <- baseline_matrix_m["D"] - delta_m_D
    
    # Update alternative transition probabilities for females
    transition_prob_f_alt1[[i]]$MPD["APD"] <- baseline_matrix_f["APD"] - delta_f_APD
    transition_prob_f_alt1[[i]]$MPD["D"] <- baseline_matrix_f["D"] - delta_f_D
  }
  return(list(transition_prob_m_alt1, transition_prob_f_alt1))
}

# Call the function to update transition probabilities
updated_transition_probs <- update_transition_probabilities(transition_prob_m, transition_prob_f, transition_prob_m_alt1, transition_prob_f_alt1)
transition_prob_m_alt <- updated_transition_probs[[1]]
transition_prob_f_alt <- updated_transition_probs[[2]]

print("Updated Transition Probabilities for Males (Alternative Scenario):")
## [1] "Updated Transition Probabilities for Males (Alternative Scenario):"
print(transition_prob_m_alt)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8717192 0.0876064 0.0406744 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87555133 0.06803194 0.05641673 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.85943770 0.05500264 0.08555966 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.79601832 0.05591376 0.14806792 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.75688830 0.04634863 0.19676307 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.68577684 0.04154472 0.27267844 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.58180832 0.02890581 0.38928587 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.43477009 0.01820149 0.54702842 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.314756967 0.009931058 0.675311974 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2304007 0.0000000 0.7695993 
## 
## [[10]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.2205748 0.0000000 0.7794252 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Updated Transition Probabilities for Females (Alternative Scenario):")
## [1] "Updated Transition Probabilities for Females (Alternative Scenario):"
print(transition_prob_f_alt)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92266989 0.05333111 0.02399900 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92675864 0.03192572 0.04131564 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91266120 0.04826618 0.03907262 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87363982 0.03898346 0.08737672 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.83033033 0.04777107 0.12189860 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.76889316 0.04168177 0.18942507 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.69217160 0.02825966 0.27956874 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.57092958 0.01604791 0.41302251 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.416392713 0.005057256 0.578550032 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3949789 0.0000000 0.6050211 
## 
## [[10]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.372703598 0.004862763 0.622433639 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
males_alt <- lapply(transition_prob_m_alt, function(prob) {
  matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
females_alt <- lapply(transition_prob_f_alt, function(prob) {
  matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})

for (i in 1:length(males_alt)) {
  colnames(males_alt[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  rownames(males_alt[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt)) {
  colnames(females_alt[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_alt[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}

print("Updated Transition Matrices for Males (Alternative Scenario):")
## [1] "Updated Transition Matrices for Males (Alternative Scenario):"
print(males_alt)
## [[1]]
##       P.m     MPD.m     APD.m        D.m
## P.m     0 0.9712352 0.0000000 0.02876483
## MPD.m   0 0.8717192 0.0876064 0.04067440
## APD.m   0 0.0000000 0.9291339 0.07086614
## D.m     0 0.0000000 0.0000000 1.00000000
## 
## [[2]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9574518 0.00000000 0.04254822
## MPD.m   0 0.8755513 0.06803194 0.05641673
## APD.m   0 0.0000000 0.87280702 0.12719298
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[3]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9433756 0.00000000 0.05662437
## MPD.m   0 0.8594377 0.05500264 0.08555966
## APD.m   0 0.0000000 0.81914894 0.18085106
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[4]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9224868 0.00000000 0.07751319
## MPD.m   0 0.7960183 0.05591376 0.14806792
## APD.m   0 0.0000000 0.69558600 0.30441400
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[5]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8875735 0.00000000 0.1124265
## MPD.m   0 0.7568883 0.04634863 0.1967631
## APD.m   0 0.0000000 0.57037037 0.4296296
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[6]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8201575 0.00000000 0.1798425
## MPD.m   0 0.6857768 0.04154472 0.2726784
## APD.m   0 0.0000000 0.48199768 0.5180023
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[7]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.7046099 0.00000000 0.2953901
## MPD.m   0 0.5818083 0.02890581 0.3892859
## APD.m   0 0.0000000 0.33866995 0.6613300
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[8]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.5279737 0.00000000 0.4720263
## MPD.m   0 0.4347701 0.01820149 0.5470284
## APD.m   0 0.0000000 0.25708502 0.7429150
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[9]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.3260733 0.000000000 0.6739267
## MPD.m   0 0.3147570 0.009931058 0.6753120
## APD.m   0 0.0000000 0.160305344 0.8396947
## D.m     0 0.0000000 0.000000000 1.0000000
## 
## [[10]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.2304007 0.0000000 0.7695993
## MPD.m   0 0.2205748 0.0000000 0.7794252
## APD.m   0 0.0000000 0.1111111 0.8888889
## D.m     0 0.0000000 0.0000000 1.0000000
print("Updated Transition Matrices for Females (Alternative Scenario):")
## [1] "Updated Transition Matrices for Females (Alternative Scenario):"
print(females_alt)
## [[1]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9864538 0.00000000 0.01354618
## MPD.f   0 0.9226699 0.05333111 0.02399900
## APD.f   0 0.0000000 0.91935484 0.08064516
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9814785 0.00000000 0.01852146
## MPD.f   0 0.9267586 0.03192572 0.04131564
## APD.f   0 0.0000000 0.86885246 0.13114754
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[3]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9750718 0.00000000 0.02492824
## MPD.f   0 0.9126612 0.04826618 0.03907262
## APD.f   0 0.0000000 0.85654008 0.14345992
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[4]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9644648 0.00000000 0.03553525
## MPD.f   0 0.8736398 0.03898346 0.08737672
## APD.f   0 0.0000000 0.77889447 0.22110553
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[5]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9455591 0.00000000 0.05444087
## MPD.f   0 0.8303303 0.04777107 0.12189860
## APD.f   0 0.0000000 0.71125265 0.28874735
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[6]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9040836 0.00000000 0.09591642
## MPD.f   0 0.7688932 0.04168177 0.18942507
## APD.f   0 0.0000000 0.61809816 0.38190184
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[7]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.8160931 0.00000000 0.1839069
## MPD.f   0 0.6921716 0.02825966 0.2795687
## APD.f   0 0.0000000 0.48024316 0.5197568
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[8]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.6559712 0.00000000 0.3440288
## MPD.f   0 0.5709296 0.01604791 0.4130225
## APD.f   0 0.0000000 0.37564767 0.6243523
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[9]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.4385294 0.000000000 0.5614706
## MPD.f   0 0.4163927 0.005057256 0.5785500
## APD.f   0 0.0000000 0.268041237 0.7319588
## D.f     0 0.0000000 0.000000000 1.0000000
## 
## [[10]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.3949789 0.000000000 0.6050211
## MPD.f   0 0.3727036 0.004862763 0.6224336
## APD.f   0 0.0000000 0.222222222 0.7777778
## D.f     0 0.0000000 0.000000000 1.0000000

The graph showcasing probabilities of remaining MPD:

extract_probabilities2_alt <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2]
    ))
    
  }
  
  return(data)
}

males_data_rem_alt <- extract_probabilities2_alt(males_alt, age_classes, "Male")
females_data_rem_alt <- extract_probabilities2_alt(females_alt, age_classes, "Female")

final_data_rem_alt <- rbind(males_data_rem_alt, females_data_rem_alt)

graph_prob_mf_rem_alt <- ggplot(final_data_rem_alt, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD with respect to gender and age classes, alternative scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_alt

The graph showcasing probabilities of transitioning from MPD to APD is:

extract_probabilities1 <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3]
    ))
    
  }
  
  return(data)
}

males_data_tra_alt <- extract_probabilities1(males_alt, age_classes, "Male")
females_data_tra_alt <- extract_probabilities1(females_alt, age_classes, "Female")

final_data_tra_alt <- rbind(males_data_tra_alt, females_data_tra_alt)

graph_prob_mf_tra_alt <- ggplot(final_data_tra_alt, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, alternative scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_alt

Comparison across scenarios (probability of remaining MPD):

extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_rem_comb <- extract_probabilities_comb1(males, age_classes, "Male", "Baseline")
females_data_rem_comb <- extract_probabilities_comb1(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_rem_alt_comb <- extract_probabilities_comb1(males_alt, age_classes, "Male", "Alternative")
females_data_rem_alt_comb <- extract_probabilities_comb1(females_alt, age_classes, "Female", "Alternative")

# Combine all data
final_data_rem_comb <- rbind(males_data_rem_comb, females_data_rem_comb, males_data_rem_alt_comb, females_data_rem_alt_comb)

# Create the combined graph
graph_prob_mf_rem_combined <- ggplot(final_data_rem_comb, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_rem_combined

Comparison across scenarios (probability of transitioning from MPD to APD):

extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_tra_comb <- extract_probabilities_comb2(males, age_classes, "Male", "Baseline")
females_data_tra_comb <- extract_probabilities_comb2(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_tra_alt_comb <- extract_probabilities_comb2(males_alt, age_classes, "Male", "Alternative")
females_data_tra_alt_comb <- extract_probabilities_comb2(females_alt, age_classes, "Female", "Alternative")

# Combine all data
final_data_tra_comb <- rbind(males_data_tra_comb, females_data_tra_comb, males_data_tra_alt_comb, females_data_tra_alt_comb)

# Create the combined graph
graph_prob_mf_tra_combined <- ggplot(final_data_tra_comb, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_tra_combined

Comparison across scenarios (probability of dying when MPD):

extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_dyingMPD = matrix[2, 4],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_die_comb <- extract_probabilities_comb3(males, age_classes, "Male", "Baseline")
females_data_die_comb <- extract_probabilities_comb3(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_die_alt_comb <- extract_probabilities_comb3(males_alt, age_classes, "Male", "Alternative")
females_data_die_alt_comb <- extract_probabilities_comb3(females_alt, age_classes, "Female", "Alternative")

# Combine all data
final_data_die_comb <- rbind(males_data_die_comb, females_data_die_comb, males_data_die_alt_comb, females_data_die_alt_comb)

# Create the combined graph
graph_prob_mf_die_combined <- ggplot(final_data_die_comb, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of dying when MPD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_die_combined

The new version of the microsimulation model is to be initialized:

n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 period

Costs in alternative scenarios are slightly different from those of the baseline scenario due to anticipation in the detection of the disease. In particular, the 1-year gain in delaying the onset of PD is associated with an early detection of 2 years (note2: why?), resulting in an early treatment of prodromal patients. All patients begin the model as prodromal in “cycle 0”, after which they either transition to MPD or pass away in “cycle 1” and this means that these patients are treated 2 years in advance before the beginning of “cycle 1”. Accordingly, the additional medical expense is equal to the 2 fifths of “c”, which is the average extra cost of a MPD patient during the 5-year cycle of the model.

(Note3: je suggérerais d’abandonner le paramètre lamdba et de supposer directement que les patients prodromiques, identifiés à l’avance comme parkinsoniens, devraient être traités comme tels en dépensant le coût total du traitement d’un patient atteint de MPD. Sinon, le traitement de ce paramètre rendrait l’analyse encore plus dispersive, car il devrait être traité pour pas moins de trois scénarios alternatifs différents)

#Males
transition_costs_m_alt <- list()
for (cycle in 1:10) {
  c.P.m <- costs_model_males[[cycle, "cp"]] + ((2/5)*costs_model_males[[cycle, "c"]])
  c.MPD.m <- costs_model_males[[cycle, "c"]]
  c.APD.m <- costs_model_males[[cycle, "C"]]
  c.D.m <- costs_model_males[[cycle, "D"]]
  transition_costs_m_alt[[cycle]] <- list(
    "P" = c(c.P.m),
    "MPD" = c(c.MPD.m),
    "APD" = c(c.APD.m),
    "D" = c(c.D.m)
  )
  
}

#Costs are repeated for 95+
last_transition_m_alt <- transition_costs_m_alt[[10]]
for (i in 11:n.t) {
  transition_costs_m_alt[[i]] <- last_transition_m_alt
}
print(transition_costs_m_alt)
## [[1]]
## [[1]]$P
## [1] 28260.64
## 
## [[1]]$MPD
## [1] 30039.15
## 
## [[1]]$APD
## [1] 82777.9
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 27026.7
## 
## [[2]]$MPD
## [1] 18805.09
## 
## [[2]]$APD
## [1] 52417.23
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 24032.15
## 
## [[3]]$MPD
## [1] 14841.59
## 
## [[3]]$APD
## [1] 54636.55
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 27575
## 
## [[4]]$MPD
## [1] 18675.96
## 
## [[4]]$APD
## [1] 46795.03
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 31487.79
## 
## [[5]]$MPD
## [1] 18764.37
## 
## [[5]]$APD
## [1] 45958.37
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 34797.93
## 
## [[6]]$MPD
## [1] 17788
## 
## [[6]]$APD
## [1] 36210.67
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 37455.06
## 
## [[7]]$MPD
## [1] 15104.06
## 
## [[7]]$APD
## [1] 33332.77
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 37602.5
## 
## [[8]]$MPD
## [1] 9020.232
## 
## [[8]]$APD
## [1] 23602.49
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 36466.5
## 
## [[9]]$MPD
## [1] 5341.272
## 
## [[9]]$APD
## [1] 19485.06
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 33886.03
## 
## [[10]]$MPD
## [1] 6355.477
## 
## [[10]]$APD
## [1] 0
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 33886.03
## 
## [[11]]$MPD
## [1] 6355.477
## 
## [[11]]$APD
## [1] 0
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 33886.03
## 
## [[12]]$MPD
## [1] 6355.477
## 
## [[12]]$APD
## [1] 0
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 33886.03
## 
## [[13]]$MPD
## [1] 6355.477
## 
## [[13]]$APD
## [1] 0
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 33886.03
## 
## [[14]]$MPD
## [1] 6355.477
## 
## [[14]]$APD
## [1] 0
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 33886.03
## 
## [[15]]$MPD
## [1] 6355.477
## 
## [[15]]$APD
## [1] 0
## 
## [[15]]$D
## [1] 0
#Females
transition_costs_f_alt <- list()
for (cycle in 1:10) {
  c.P.f <- costs_model_females[[cycle, "cp"]] + ((2/5)*costs_model_females[[cycle, "c"]])
  c.MPD.f <- costs_model_females[[cycle, "c"]]
  c.APD.f <- costs_model_females[[cycle, "C"]]
  c.D.f <- costs_model_females[[cycle, "D"]]
  transition_costs_f_alt[[cycle]] <- list(
    "P" = c(c.P.f),
    "MPD" = c(c.MPD.f),
    "APD" = c(c.APD.f),
    "D" = c(c.D.f)
  )
  
}

#Costs are repeated for 95+
last_transition_f_alt <- transition_costs_f_alt[[10]]
for (i in 11:n.t) {
  transition_costs_f_alt[[i]] <- last_transition_f_alt
}

print(transition_costs_f_alt)
## [[1]]
## [[1]]$P
## [1] 25124.56
## 
## [[1]]$MPD
## [1] 24292.53
## 
## [[1]]$APD
## [1] 55993.02
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 26874.58
## 
## [[2]]$MPD
## [1] 24368.35
## 
## [[2]]$APD
## [1] 66431.63
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 21895.67
## 
## [[3]]$MPD
## [1] 16594.83
## 
## [[3]]$APD
## [1] 64962.58
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 22633.31
## 
## [[4]]$MPD
## [1] 15286.68
## 
## [[4]]$APD
## [1] 50340.51
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 28864.52
## 
## [[5]]$MPD
## [1] 21780.85
## 
## [[5]]$APD
## [1] 34621.54
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 31653.34
## 
## [[6]]$MPD
## [1] 18533.03
## 
## [[6]]$APD
## [1] 41807.45
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 36832.21
## 
## [[7]]$MPD
## [1] 19459.15
## 
## [[7]]$APD
## [1] 42848.83
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 38166.8
## 
## [[8]]$MPD
## [1] 12637.32
## 
## [[8]]$APD
## [1] 34938.64
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 35370.47
## 
## [[9]]$MPD
## [1] 2801.658
## 
## [[9]]$APD
## [1] 35427.99
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 30843.99
## 
## [[10]]$MPD
## [1] 0
## 
## [[10]]$APD
## [1] 11693.52
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 30843.99
## 
## [[11]]$MPD
## [1] 0
## 
## [[11]]$APD
## [1] 11693.52
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 30843.99
## 
## [[12]]$MPD
## [1] 0
## 
## [[12]]$APD
## [1] 11693.52
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 30843.99
## 
## [[13]]$MPD
## [1] 0
## 
## [[13]]$APD
## [1] 11693.52
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 30843.99
## 
## [[14]]$MPD
## [1] 0
## 
## [[14]]$APD
## [1] 11693.52
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 30843.99
## 
## [[15]]$MPD
## [1] 0
## 
## [[15]]$APD
## [1] 11693.52
## 
## [[15]]$D
## [1] 0

The microsimulation function for male patients is:

m.M <- m.C <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_males
#Males
Probs <- function(state){
  return(transition_prob_m_alt[[state]])
}
Costs <- function(state) {
  return(transition_costs_m[[state]])
}

# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_m_alt <- transition_prob_m_alt %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_alt <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M_alt[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_m_alt[[10]][[current_state]]), 1, prob = transition_prob_m_alt[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_m_alt[[t]][[current_state]]), 1, prob = transition_prob_m_alt[[t]][[current_state]])
         }
        m.M_alt[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M_alt)
}

# Init m.M #repeat it!!!!
model_results_m_alt <- list()
for(i in 1:n.sim) {
m.M_alt <- m.C_alt <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M_alt[, 1] <- v.M_1_males
# Microsim loop
model_results_m_alt[[i]] <- loop_microsim_alt(n.t)
print(i)
} 
## [1] 1
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## [1] 100
# repeat it!!!


#Results of the median simulation, the 50th
model_results_m_alt[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 2   "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 3   "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 5   "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 7   "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 8   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 9   "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 11  "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 15  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 17  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 18  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 19  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 22  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 26  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 30  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 31  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 32  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 33  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 38  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 43  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 48  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 53  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 55  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 60  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 61  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 64  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 65  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 66  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 70  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 71  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 72  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 74  "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 76  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 80  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 81  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 82  "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 85  "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 87  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 90  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 92  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 94  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 96  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 97  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 98  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 101 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 102 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 104 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 105 "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 107 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 108 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 112 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 116 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 117 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 120 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 124 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 125 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 128 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 129 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 132 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 133 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 135 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 137 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 139 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 141 "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 143 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 145 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 152 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 155 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 156 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 164 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 165 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 166 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 168 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 173 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 174 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 178 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 179 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 182 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 188 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 190 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 196 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 197 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 198 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 203 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 204 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 206 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 207 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 208 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 209 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 211 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 213 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 214 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 217 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 220 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 224 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 228 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 230 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 233 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 235 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 238 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 239 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 241 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 243 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 244 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 250 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 255 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 258 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 259 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 267 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 268 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 269 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 270 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 272 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 273 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 278 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 279 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 281 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 287 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 294 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 296 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 299 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 2   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 20  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 21  "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 22  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 23  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 24  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 25  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 26  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 27  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 28  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 29  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 30  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 31  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 32  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 33  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 34  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 35  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 36  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 37  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 38  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 39  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 40  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 41  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 42  "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 43  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 44  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 45  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 46  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 47  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 48  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 49  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 50  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 51  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 52  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 53  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 54  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 55  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 56  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 57  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 58  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 59  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 60  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 61  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 62  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 63  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 64  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 65  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 66  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 67  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 68  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 69  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 70  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 71  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 72  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 73  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 74  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 75  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 76  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 77  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 78  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 79  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 80  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 81  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 82  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 83  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 84  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 85  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 86  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 87  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 88  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 89  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 90  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 91  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 92  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 93  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 94  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 95  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 96  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 97  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 98  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 99  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 100 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 101 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 102 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 103 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 104 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 105 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 106 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 107 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 108 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 109 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 110 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 111 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 112 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 113 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 114 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 115 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 116 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 117 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 118 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 119 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 120 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 121 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 122 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 123 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 124 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 125 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 126 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 127 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 128 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 129 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 130 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 131 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 132 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 133 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 134 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 135 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 136 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 137 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 138 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 139 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 140 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 141 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 142 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 143 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 144 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 145 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 146 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 147 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 148 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 149 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 150 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 151 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 152 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 153 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 154 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 155 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 156 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 157 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 158 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 159 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 160 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 161 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 162 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 163 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 164 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 165 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 166 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 167 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 168 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 169 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 170 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 171 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 172 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 173 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 174 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 175 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 176 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 177 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 178 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 179 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 180 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 181 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 182 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 183 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 184 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 185 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 186 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 187 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 188 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 189 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 190 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 191 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 192 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 193 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 194 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 195 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 196 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 197 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 198 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 199 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 200 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 201 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 202 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 203 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 204 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 205 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 206 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 207 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 208 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 209 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 210 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 211 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 212 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 213 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 214 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 215 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 216 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 228 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 229 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 230 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 231 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 235 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 236 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 237 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 238 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 241 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 242 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 243 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 244 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 245 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 246 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 247 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 248 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 249 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 250 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 256 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 257 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 258 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 260 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 262 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 263 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 264 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 265 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 266 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 267 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 268 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 269 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 270 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 271 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 272 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 273 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 274 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 275 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 276 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 277 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 278 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 279 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 280 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 281 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 282 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 283 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 284 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 285 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 286 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 287 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 288 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 289 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 290 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 291 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 292 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 293 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 294 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 295 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 296 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 297 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 298 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 299 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 300 "D"     "D"      "D"      "D"      "D"      "D"      "D"
df_m.M_alt <- model_results_m_alt[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M_alt %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 15600       1
## 
## [[2]]
##  cycle 1     n    percent
##        D   475 0.03044872
##      MPD 15125 0.96955128
## 
## [[3]]
##  cycle 2     n    percent
##      APD   972 0.06230769
##        D  1297 0.08314103
##      MPD 13331 0.85455128
## 
## [[4]]
##  cycle 3     n    percent
##      APD  1536 0.09846154
##        D  2658 0.17038462
##      MPD 11406 0.73115385
## 
## [[5]]
##  cycle 4    n   percent
##      APD 1709 0.1095513
##        D 4849 0.3108333
##      MPD 9042 0.5796154
## 
## [[6]]
##  cycle 5    n    percent
##      APD 1388 0.08897436
##        D 7454 0.47782051
##      MPD 6758 0.43320513
## 
## [[7]]
##  cycle 6     n    percent
##      APD   959 0.06147436
##        D 10005 0.64134615
##      MPD  4636 0.29717949
## 
## [[8]]
##  cycle 7     n    percent
##      APD   457 0.02929487
##        D 12429 0.79673077
##      MPD  2714 0.17397436
## 
## [[9]]
##  cycle 8     n     percent
##      APD   146 0.009358974
##        D 14245 0.913141026
##      MPD  1209 0.077500000
## 
## [[10]]
##  cycle 9     n     percent
##      APD    44 0.002820513
##        D 15176 0.972820513
##      MPD   380 0.024358974
## 
## [[11]]
##  cycle 10     n      percent
##       APD     4 0.0002564103
##         D 15510 0.9942307692
##       MPD    86 0.0055128205
## 
## [[12]]
##  cycle 11     n      percent
##       APD     3 0.0001923077
##         D 15581 0.9987820513
##       MPD    16 0.0010256410
## 
## [[13]]
##  cycle 12     n      percent
##         D 15593 0.9995512821
##       MPD     7 0.0004487179
## 
## [[14]]
##  cycle 13     n      percent
##         D 15597 0.9998076923
##       MPD     3 0.0001923077
## 
## [[15]]
##  cycle 14     n       percent
##         D 15599 0.99993589744
##       MPD     1 0.00006410256
# Transition costs in a dataframe
transition_costs_m_alt <-
 transition_costs_m_alt %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
   "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")


final_cost_m_alt <-
  map(
    model_results_m_alt,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_m_alt
      )
  )
  

final_cost_m2_alt <-
  map(
    final_cost_m_alt,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
     filter(cycle != "cycle 15")
  )
final_cost_m2_alt
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 253799562.
##  4 cycle 3  15600 288309089.
##  5 cycle 4  15600 252169466.
##  6 cycle 5  15600 174190006.
##  7 cycle 6  15600 103422936.
##  8 cycle 7  15600  34906291.
##  9 cycle 8  15600   9429411.
## 10 cycle 9  15600   2408726.
## 11 cycle 10 15600    597415.
## 12 cycle 11 15600    152531.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 250284720.
##  4 cycle 3  15600 284527083.
##  5 cycle 4  15600 247879575.
##  6 cycle 5  15600 170833854.
##  7 cycle 6  15600 103555758.
##  8 cycle 7  15600  36761433.
##  9 cycle 8  15600  10128607.
## 10 cycle 9  15600   2294327.
## 11 cycle 10 15600    540216.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 253074446.
##  4 cycle 3  15600 286006479.
##  5 cycle 4  15600 249445447.
##  6 cycle 5  15600 172294340.
##  7 cycle 6  15600 102228677.
##  8 cycle 7  15600  35277474.
##  9 cycle 8  15600   9488850.
## 10 cycle 9  15600   2465925.
## 11 cycle 10 15600    552926.
## 12 cycle 11 15600    133465.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285122707.
##  3 cycle 2  15600 253409116.
##  4 cycle 3  15600 287439500.
##  5 cycle 4  15600 250476627.
##  6 cycle 5  15600 172678678.
##  7 cycle 6  15600 102397946.
##  8 cycle 7  15600  35385717.
##  9 cycle 8  15600   9844238.
## 10 cycle 9  15600   2516769.
## 11 cycle 10 15600    571993.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 253393458.
##  4 cycle 3  15600 284879422.
##  5 cycle 4  15600 247705503.
##  6 cycle 5  15600 171893467.
##  7 cycle 6  15600 101860968.
##  8 cycle 7  15600  36036357.
##  9 cycle 8  15600   9701604.
## 10 cycle 9  15600   2383304.
## 11 cycle 10 15600    470305.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285085097.
##  3 cycle 2  15600 252485024.
##  4 cycle 3  15600 287659827.
##  5 cycle 4  15600 251245157.
##  6 cycle 5  15600 174568648.
##  7 cycle 6  15600 104478653.
##  8 cycle 7  15600  36022090.
##  9 cycle 8  15600   9711602.
## 10 cycle 9  15600   2376948.
## 11 cycle 10 15600    489372.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285799690.
##  3 cycle 2  15600 252335956.
##  4 cycle 3  15600 287736003.
##  5 cycle 4  15600 251494809.
##  6 cycle 5  15600 174731279.
##  7 cycle 6  15600 105199476.
##  8 cycle 7  15600  37483341.
##  9 cycle 8  15600  10071136.
## 10 cycle 9  15600   2249839.
## 11 cycle 10 15600    483016.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 253155504.
##  4 cycle 3  15600 286803256.
##  5 cycle 4  15600 249390199.
##  6 cycle 5  15600 173838054.
##  7 cycle 6  15600 103982815.
##  8 cycle 7  15600  35574537.
##  9 cycle 8  15600   9981828.
## 10 cycle 9  15600   2567613.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 252762120.
##  4 cycle 3  15600 285326594.
##  5 cycle 4  15600 250296604.
##  6 cycle 5  15600 174489846.
##  7 cycle 6  15600 104114060.
##  8 cycle 7  15600  36206847.
##  9 cycle 8  15600   9647208.
## 10 cycle 9  15600   2313394.
## 11 cycle 10 15600    483016.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285574029.
##  3 cycle 2  15600 256830831.
##  4 cycle 3  15600 288995702.
##  5 cycle 4  15600 253537596.
##  6 cycle 5  15600 176422357.
##  7 cycle 6  15600 105041657.
##  8 cycle 7  15600  37160427.
##  9 cycle 8  15600   9767194.
## 10 cycle 9  15600   2472280.
## 11 cycle 10 15600    502083.
## 12 cycle 11 15600    139820.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 253880457.
##  4 cycle 3  15600 288679052.
##  5 cycle 4  15600 252013350.
##  6 cycle 5  15600 175025373.
##  7 cycle 6  15600 104404681.
##  8 cycle 7  15600  35884656.
##  9 cycle 8  15600   9777665.
## 10 cycle 9  15600   2313394.
## 11 cycle 10 15600    476661.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 255162051.
##  4 cycle 3  15600 289171968.
##  5 cycle 4  15600 250180208.
##  6 cycle 5  15600 173404795.
##  7 cycle 6  15600 102892727.
##  8 cycle 7  15600  35569869.
##  9 cycle 8  15600   9530597.
## 10 cycle 9  15600   2478636.
## 11 cycle 10 15600    597415.
## 12 cycle 11 15600    127110.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284314088.
##  3 cycle 2  15600 253171486.
##  4 cycle 3  15600 287330598.
##  5 cycle 4  15600 252013636.
##  6 cycle 5  15600 173564887.
##  7 cycle 6  15600 104046885.
##  8 cycle 7  15600  35696152.
##  9 cycle 8  15600   9765015.
## 10 cycle 9  15600   2357882.
## 11 cycle 10 15600    508438.
## 12 cycle 11 15600    127110.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285461198.
##  3 cycle 2  15600 252789194.
##  4 cycle 3  15600 287410330.
##  5 cycle 4  15600 247602729.
##  6 cycle 5  15600 172925823.
##  7 cycle 6  15600 103233880.
##  8 cycle 7  15600  34505048.
##  9 cycle 8  15600   9348695.
## 10 cycle 9  15600   2376948.
## 11 cycle 10 15600    470305.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 253117502.
##  4 cycle 3  15600 286067133.
##  5 cycle 4  15600 251229343.
##  6 cycle 5  15600 171312827.
##  7 cycle 6  15600 102822936.
##  8 cycle 7  15600  35467938.
##  9 cycle 8  15600   9408731.
## 10 cycle 9  15600   2376948.
## 11 cycle 10 15600    489372.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 253701705.
##  4 cycle 3  15600 287635282.
##  5 cycle 4  15600 250502725.
##  6 cycle 5  15600 171702259.
##  7 cycle 6  15600 101219319.
##  8 cycle 7  15600  34815194.
##  9 cycle 8  15600  10029900.
## 10 cycle 9  15600   2510413.
## 11 cycle 10 15600    591059.
## 12 cycle 11 15600    165242.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 253974236.
##  4 cycle 3  15600 285111715.
##  5 cycle 4  15600 248034023.
##  6 cycle 5  15600 172476629.
##  7 cycle 6  15600 101770344.
##  8 cycle 7  15600  35374277.
##  9 cycle 8  15600   9441077.
## 10 cycle 9  15600   2415081.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600     95332.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 252086258.
##  4 cycle 3  15600 285093460.
##  5 cycle 4  15600 247976920.
##  6 cycle 5  15600 172693910.
##  7 cycle 6  15600 102580233.
##  8 cycle 7  15600  35437735.
##  9 cycle 8  15600   9995972.
## 10 cycle 9  15600   2535835.
## 11 cycle 10 15600    508438.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 250849353.
##  4 cycle 3  15600 285994723.
##  5 cycle 4  15600 250185400.
##  6 cycle 5  15600 172665984.
##  7 cycle 6  15600 102847434.
##  8 cycle 7  15600  35611972.
##  9 cycle 8  15600   9737026.
## 10 cycle 9  15600   2421437.
## 11 cycle 10 15600    527505.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284803020.
##  3 cycle 2  15600 252655620.
##  4 cycle 3  15600 287048146.
##  5 cycle 4  15600 249731768.
##  6 cycle 5  15600 172461431.
##  7 cycle 6  15600 102971391.
##  8 cycle 7  15600  37295126.
##  9 cycle 8  15600  10417123.
## 10 cycle 9  15600   2478636.
## 11 cycle 10 15600    489372.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284276478.
##  3 cycle 2  15600 253807553.
##  4 cycle 3  15600 284036481.
##  5 cycle 4  15600 249271088.
##  6 cycle 5  15600 172627835.
##  7 cycle 6  15600 103888547.
##  8 cycle 7  15600  35406755.
##  9 cycle 8  15600   9758777.
## 10 cycle 9  15600   2249839.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 254570508.
##  4 cycle 3  15600 288705910.
##  5 cycle 4  15600 252370446.
##  6 cycle 5  15600 175931960.
##  7 cycle 6  15600 105103650.
##  8 cycle 7  15600  36001341.
##  9 cycle 8  15600   9430694.
## 10 cycle 9  15600   2376948.
## 11 cycle 10 15600    502083.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 253734814.
##  4 cycle 3  15600 286148126.
##  5 cycle 4  15600 250613693.
##  6 cycle 5  15600 173646211.
##  7 cycle 6  15600 102996898.
##  8 cycle 7  15600  35799122.
##  9 cycle 8  15600   9534743.
## 10 cycle 9  15600   2535835.
## 11 cycle 10 15600    552926.
## 12 cycle 11 15600    127110.
## 13 cycle 12 15600     50844.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284746605.
##  3 cycle 2  15600 254624981.
##  4 cycle 3  15600 285205936.
##  5 cycle 4  15600 250680034.
##  6 cycle 5  15600 174675376.
##  7 cycle 6  15600 103547941.
##  8 cycle 7  15600  35672550.
##  9 cycle 8  15600   9283317.
## 10 cycle 9  15600   2408726.
## 11 cycle 10 15600    483016.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 252489589.
##  4 cycle 3  15600 286754989.
##  5 cycle 4  15600 249602609.
##  6 cycle 5  15600 172932804.
##  7 cycle 6  15600 102937520.
##  8 cycle 7  15600  35610907.
##  9 cycle 8  15600   9481928.
## 10 cycle 9  15600   2326104.
## 11 cycle 10 15600    552926.
## 12 cycle 11 15600    133465.
## 13 cycle 12 15600     57199.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 256367480.
##  4 cycle 3  15600 290960463.
##  5 cycle 4  15600 253720620.
##  6 cycle 5  15600 175616185.
##  7 cycle 6  15600 106395812.
##  8 cycle 7  15600  36378376.
##  9 cycle 8  15600   9863723.
## 10 cycle 9  15600   2453214.
## 11 cycle 10 15600    508438.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 250993527.
##  4 cycle 3  15600 285698431.
##  5 cycle 4  15600 247670975.
##  6 cycle 5  15600 171666683.
##  7 cycle 6  15600 101026620.
##  8 cycle 7  15600  34516172.
##  9 cycle 8  15600   9784201.
## 10 cycle 9  15600   2249839.
## 11 cycle 10 15600    502083.
## 12 cycle 11 15600     95332.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284878241.
##  3 cycle 2  15600 251383486.
##  4 cycle 3  15600 285150959.
##  5 cycle 4  15600 245085876.
##  6 cycle 5  15600 171237232.
##  7 cycle 6  15600 101225050.
##  8 cycle 7  15600  35306783.
##  9 cycle 8  15600   9408731.
## 10 cycle 9  15600   2110018.
## 11 cycle 10 15600    470305.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 253652615.
##  4 cycle 3  15600 287824565.
##  5 cycle 4  15600 250067335.
##  6 cycle 5  15600 175180404.
##  7 cycle 6  15600 104317710.
##  8 cycle 7  15600  37018957.
##  9 cycle 8  15600   9896667.
## 10 cycle 9  15600   2484991.
## 11 cycle 10 15600    533860.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 255538960.
##  4 cycle 3  15600 288697938.
##  5 cycle 4  15600 250337606.
##  6 cycle 5  15600 169945673.
##  7 cycle 6  15600  99773492.
##  8 cycle 7  15600  34086659.
##  9 cycle 8  15600   9167689.
## 10 cycle 9  15600   2224417.
## 11 cycle 10 15600    514794.
## 12 cycle 11 15600    165242.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 253362470.
##  4 cycle 3  15600 287528483.
##  5 cycle 4  15600 250037662.
##  6 cycle 5  15600 170266508.
##  7 cycle 6  15600 101809407.
##  8 cycle 7  15600  34927184.
##  9 cycle 8  15600   9417534.
## 10 cycle 9  15600   2421437.
## 11 cycle 10 15600    514794.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 251428663.
##  4 cycle 3  15600 285766004.
##  5 cycle 4  15600 248888754.
##  6 cycle 5  15600 173199607.
##  7 cycle 6  15600 103393247.
##  8 cycle 7  15600  36117394.
##  9 cycle 8  15600   9674213.
## 10 cycle 9  15600   2440503.
## 11 cycle 10 15600    514794.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 253914217.
##  4 cycle 3  15600 287029470.
##  5 cycle 4  15600 252028877.
##  6 cycle 5  15600 175293462.
##  7 cycle 6  15600 103856772.
##  8 cycle 7  15600  35954425.
##  9 cycle 8  15600   9847400.
## 10 cycle 9  15600   2237128.
## 11 cycle 10 15600    508438.
## 12 cycle 11 15600    152531.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 256927219.
##  4 cycle 3  15600 289858161.
##  5 cycle 4  15600 252025876.
##  6 cycle 5  15600 172132344.
##  7 cycle 6  15600 101555243.
##  8 cycle 7  15600  34698076.
##  9 cycle 8  15600   9636526.
## 10 cycle 9  15600   2504058.
## 11 cycle 10 15600    489372.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 253102172.
##  4 cycle 3  15600 287193769.
##  5 cycle 4  15600 252215998.
##  6 cycle 5  15600 174960602.
##  7 cycle 6  15600 102637015.
##  8 cycle 7  15600  36460163.
##  9 cycle 8  15600  10161937.
## 10 cycle 9  15600   2370593.
## 11 cycle 10 15600    578348.
## 12 cycle 11 15600    152531.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 255439311.
##  4 cycle 3  15600 289193167.
##  5 cycle 4  15600 252038638.
##  6 cycle 5  15600 172526202.
##  7 cycle 6  15600 103667716.
##  8 cycle 7  15600  35836413.
##  9 cycle 8  15600  10090023.
## 10 cycle 9  15600   2307038.
## 11 cycle 10 15600    425817.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 250464612.
##  4 cycle 3  15600 283294680.
##  5 cycle 4  15600 247754462.
##  6 cycle 5  15600 171735296.
##  7 cycle 6  15600 102045880.
##  8 cycle 7  15600  34947789.
##  9 cycle 8  15600   9359377.
## 10 cycle 9  15600   2586679.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 254416058.
##  4 cycle 3  15600 287871991.
##  5 cycle 4  15600 252415308.
##  6 cycle 5  15600 173516601.
##  7 cycle 6  15600 104584362.
##  8 cycle 7  15600  35898200.
##  9 cycle 8  15600   9724165.
## 10 cycle 9  15600   2453214.
## 11 cycle 10 15600    540216.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285555224.
##  3 cycle 2  15600 253175076.
##  4 cycle 3  15600 290152771.
##  5 cycle 4  15600 252237999.
##  6 cycle 5  15600 174381197.
##  7 cycle 6  15600 103497918.
##  8 cycle 7  15600  34171905.
##  9 cycle 8  15600   9116754.
## 10 cycle 9  15600   2224417.
## 11 cycle 10 15600    495727.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     50844.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600      6355.
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284238868.
##  3 cycle 2  15600 251918923.
##  4 cycle 3  15600 286654689.
##  5 cycle 4  15600 250359135.
##  6 cycle 5  15600 172188230.
##  7 cycle 6  15600 101825030.
##  8 cycle 7  15600  34749488.
##  9 cycle 8  15600   9375999.
## 10 cycle 9  15600   2415081.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 255437842.
##  4 cycle 3  15600 288989624.
##  5 cycle 4  15600 254042042.
##  6 cycle 5  15600 175271849.
##  7 cycle 6  15600 102982303.
##  8 cycle 7  15600  35527016.
##  9 cycle 8  15600   9353139.
## 10 cycle 9  15600   2249839.
## 11 cycle 10 15600    559282.
## 12 cycle 11 15600    146176.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 252552381.
##  4 cycle 3  15600 285296372.
##  5 cycle 4  15600 250349896.
##  6 cycle 5  15600 173482294.
##  7 cycle 6  15600 101605257.
##  8 cycle 7  15600  34358621.
##  9 cycle 8  15600   9801806.
## 10 cycle 9  15600   2421437.
## 11 cycle 10 15600    476661.
## 12 cycle 11 15600     95332.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 252828989.
##  4 cycle 3  15600 289039133.
##  5 cycle 4  15600 252589616.
##  6 cycle 5  15600 173912379.
##  7 cycle 6  15600 104476048.
##  8 cycle 7  15600  35632117.
##  9 cycle 8  15600   9902693.
## 10 cycle 9  15600   2484991.
## 11 cycle 10 15600    578348.
## 12 cycle 11 15600    133465.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 252468061.
##  4 cycle 3  15600 285101431.
##  5 cycle 4  15600 248615145.
##  6 cycle 5  15600 171727663.
##  7 cycle 6  15600 102151080.
##  8 cycle 7  15600  34225277.
##  9 cycle 8  15600   9603196.
## 10 cycle 9  15600   2307038.
## 11 cycle 10 15600    438528.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 251580339.
##  4 cycle 3  15600 286011946.
##  5 cycle 4  15600 247382699.
##  6 cycle 5  15600 172335611.
##  7 cycle 6  15600 102205756.
##  8 cycle 7  15600  35363153.
##  9 cycle 8  15600   9903975.
## 10 cycle 9  15600   2427792.
## 11 cycle 10 15600    425817.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 254845648.
##  4 cycle 3  15600 285496359.
##  5 cycle 4  15600 249484021.
##  6 cycle 5  15600 171531360.
##  7 cycle 6  15600 101894311.
##  8 cycle 7  15600  35619178.
##  9 cycle 8  15600   9672333.
## 10 cycle 9  15600   2510413.
## 11 cycle 10 15600    533860.
## 12 cycle 11 15600    158887.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 251929687.
##  4 cycle 3  15600 285936401.
##  5 cycle 4  15600 248673630.
##  6 cycle 5  15600 175228039.
##  7 cycle 6  15600 104011456.
##  8 cycle 7  15600  36461951.
##  9 cycle 8  15600   9976574.
## 10 cycle 9  15600   2249839.
## 11 cycle 10 15600    565637.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 253421836.
##  4 cycle 3  15600 287235536.
##  5 cycle 4  15600 248904804.
##  6 cycle 5  15600 171825522.
##  7 cycle 6  15600 102838050.
##  8 cycle 7  15600  36208951.
##  9 cycle 8  15600   9945124.
## 10 cycle 9  15600   2338815.
## 11 cycle 10 15600    495727.
## 12 cycle 11 15600    127110.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600     19066.
## 15 cycle 14 15600      6355.
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 253592432.
##  4 cycle 3  15600 287995172.
##  5 cycle 4  15600 249194935.
##  6 cycle 5  15600 171576439.
##  7 cycle 6  15600 102526576.
##  8 cycle 7  15600  35588659.
##  9 cycle 8  15600   9451760.
## 10 cycle 9  15600   2307038.
## 11 cycle 10 15600    438528.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 250959930.
##  4 cycle 3  15600 284895154.
##  5 cycle 4  15600 248210236.
##  6 cycle 5  15600 170471697.
##  7 cycle 6  15600 101988569.
##  8 cycle 7  15600  35267244.
##  9 cycle 8  15600   9302416.
## 10 cycle 9  15600   2415081.
## 11 cycle 10 15600    546571.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600     19066.
## 15 cycle 14 15600      6355.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285216732.
##  3 cycle 2  15600 253697140.
##  4 cycle 3  15600 287823724.
##  5 cycle 4  15600 251253013.
##  6 cycle 5  15600 175178483.
##  7 cycle 6  15600 103464595.
##  8 cycle 7  15600  36937314.
##  9 cycle 8  15600   9898248.
## 10 cycle 9  15600   2408726.
## 11 cycle 10 15600    559282.
## 12 cycle 11 15600    152531.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 253581016.
##  4 cycle 3  15600 287197764.
##  5 cycle 4  15600 251183671.
##  6 cycle 5  15600 173970821.
##  7 cycle 6  15600 104122395.
##  8 cycle 7  15600  35155399.
##  9 cycle 8  15600  10010801.
## 10 cycle 9  15600   2281616.
## 11 cycle 10 15600    489372.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 254152335.
##  4 cycle 3  15600 288833487.
##  5 cycle 4  15600 252140654.
##  6 cycle 5  15600 175030484.
##  7 cycle 6  15600 104030223.
##  8 cycle 7  15600  36293591.
##  9 cycle 8  15600   9284811.
## 10 cycle 9  15600   2383304.
## 11 cycle 10 15600    648259.
## 12 cycle 11 15600    184309.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284897046.
##  3 cycle 2  15600 252567223.
##  4 cycle 3  15600 284088303.
##  5 cycle 4  15600 249791062.
##  6 cycle 5  15600 172622140.
##  7 cycle 6  15600 102373997.
##  8 cycle 7  15600  36106270.
##  9 cycle 8  15600   9487866.
## 10 cycle 9  15600   2294327.
## 11 cycle 10 15600    584704.
## 12 cycle 11 15600    197020.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 254140102.
##  4 cycle 3  15600 285440121.
##  5 cycle 4  15600 247401750.
##  6 cycle 5  15600 172733929.
##  7 cycle 6  15600 104925516.
##  8 cycle 7  15600  36538925.
##  9 cycle 8  15600  10107242.
## 10 cycle 9  15600   2402370.
## 11 cycle 10 15600    457594.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600     12711.
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285592834.
##  3 cycle 2  15600 252377708.
##  4 cycle 3  15600 286547451.
##  5 cycle 4  15600 250519062.
##  6 cycle 5  15600 174979676.
##  7 cycle 6  15600 105310415.
##  8 cycle 7  15600  36031544.
##  9 cycle 8  15600   9926536.
## 10 cycle 9  15600   2396015.
## 11 cycle 10 15600    451239.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 253635001.
##  4 cycle 3  15600 286410851.
##  5 cycle 4  15600 248618955.
##  6 cycle 5  15600 175553918.
##  7 cycle 6  15600 104642702.
##  8 cycle 7  15600  35501454.
##  9 cycle 8  15600   9658277.
## 10 cycle 9  15600   2224417.
## 11 cycle 10 15600    508438.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285310758.
##  3 cycle 2  15600 254414102.
##  4 cycle 3  15600 287442023.
##  5 cycle 4  15600 251929862.
##  6 cycle 5  15600 176141574.
##  7 cycle 6  15600 105232799.
##  8 cycle 7  15600  35544768.
##  9 cycle 8  15600   9636526.
## 10 cycle 9  15600   2535835.
## 11 cycle 10 15600    565637.
## 12 cycle 11 15600    139820.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 252708136.
##  4 cycle 3  15600 286058110.
##  5 cycle 4  15600 246514918.
##  6 cycle 5  15600 171882643.
##  7 cycle 6  15600 101228155.
##  8 cycle 7  15600  34693868.
##  9 cycle 8  15600   9920895.
## 10 cycle 9  15600   2326104.
## 11 cycle 10 15600    451239.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 253394762.
##  4 cycle 3  15600 285000080.
##  5 cycle 4  15600 246204640.
##  6 cycle 5  15600 168694818.
##  7 cycle 6  15600 100612023.
##  8 cycle 7  15600  35279867.
##  9 cycle 8  15600   9953628.
## 10 cycle 9  15600   2376948.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600    133465.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 252837797.
##  4 cycle 3  15600 287982365.
##  5 cycle 4  15600 251993540.
##  6 cycle 5  15600 173371775.
##  7 cycle 6  15600 104130741.
##  8 cycle 7  15600  36133475.
##  9 cycle 8  15600  10387254.
## 10 cycle 9  15600   2516769.
## 11 cycle 10 15600    463950.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 252124748.
##  4 cycle 3  15600 287635703.
##  5 cycle 4  15600 249719528.
##  6 cycle 5  15600 172220632.
##  7 cycle 6  15600 102600548.
##  8 cycle 7  15600  34606519.
##  9 cycle 8  15600   9584395.
## 10 cycle 9  15600   2440503.
## 11 cycle 10 15600    489372.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 253632227.
##  4 cycle 3  15600 287402989.
##  5 cycle 4  15600 249849783.
##  6 cycle 5  15600 170966604.
##  7 cycle 6  15600 102331800.
##  8 cycle 7  15600  35557390.
##  9 cycle 8  15600   9750660.
## 10 cycle 9  15600   2135440.
## 11 cycle 10 15600    463950.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 253686864.
##  4 cycle 3  15600 286976596.
##  5 cycle 4  15600 248401403.
##  6 cycle 5  15600 171315332.
##  7 cycle 6  15600 101251075.
##  8 cycle 7  15600  34393492.
##  9 cycle 8  15600   9164527.
## 10 cycle 9  15600   2484991.
## 11 cycle 10 15600    470305.
## 12 cycle 11 15600    165242.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     19066.
## 15 cycle 14 15600     12711.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 251293132.
##  4 cycle 3  15600 285361843.
##  5 cycle 4  15600 253520501.
##  6 cycle 5  15600 176278166.
##  7 cycle 6  15600 105932278.
##  8 cycle 7  15600  36780828.
##  9 cycle 8  15600  10254320.
## 10 cycle 9  15600   2396015.
## 11 cycle 10 15600    552926.
## 12 cycle 11 15600    165242.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 252781039.
##  4 cycle 3  15600 286222199.
##  5 cycle 4  15600 248534136.
##  6 cycle 5  15600 173879994.
##  7 cycle 6  15600 102764097.
##  8 cycle 7  15600  36374168.
##  9 cycle 8  15600   9690835.
## 10 cycle 9  15600   2478636.
## 11 cycle 10 15600    565637.
## 12 cycle 11 15600    139820.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600     25422.
## 15 cycle 14 15600         0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 252694763.
##  4 cycle 3  15600 287188952.
##  5 cycle 4  15600 252135225.
##  6 cycle 5  15600 175381784.
##  7 cycle 6  15600 104586978.
##  8 cycle 7  15600  35569580.
##  9 cycle 8  15600   9637721.
## 10 cycle 9  15600   2523124.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 253537144.
##  4 cycle 3  15600 285785311.
##  5 cycle 4  15600 251307688.
##  6 cycle 5  15600 174571170.
##  7 cycle 6  15600 104453647.
##  8 cycle 7  15600  35532145.
##  9 cycle 8  15600   9929399.
## 10 cycle 9  15600   2237128.
## 11 cycle 10 15600    540216.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 251435185.
##  4 cycle 3  15600 283307067.
##  5 cycle 4  15600 247255731.
##  6 cycle 5  15600 171205464.
##  7 cycle 6  15600 101420874.
##  8 cycle 7  15600  35215976.
##  9 cycle 8  15600   8735556.
## 10 cycle 9  15600   2294327.
## 11 cycle 10 15600    470305.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284972266.
##  3 cycle 2  15600 255394134.
##  4 cycle 3  15600 288758363.
##  5 cycle 4  15600 250828817.
##  6 cycle 5  15600 175162633.
##  7 cycle 6  15600 104166160.
##  8 cycle 7  15600  36825180.
##  9 cycle 8  15600   9999433.
## 10 cycle 9  15600   2345171.
## 11 cycle 10 15600    495727.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 252463984.
##  4 cycle 3  15600 283985480.
##  5 cycle 4  15600 245706196.
##  6 cycle 5  15600 173277106.
##  7 cycle 6  15600 102759915.
##  8 cycle 7  15600  34711159.
##  9 cycle 8  15600   9371853.
## 10 cycle 9  15600   2326104.
## 11 cycle 10 15600    584704.
## 12 cycle 11 15600    146176.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 254341034.
##  4 cycle 3  15600 289243536.
##  5 cycle 4  15600 252865602.
##  6 cycle 5  15600 174905283.
##  7 cycle 6  15600 102742196.
##  8 cycle 7  15600  35617968.
##  9 cycle 8  15600   9820395.
## 10 cycle 9  15600   2370593.
## 11 cycle 10 15600    527505.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 252584838.
##  4 cycle 3  15600 286692041.
##  5 cycle 4  15600 250803242.
##  6 cycle 5  15600 171490724.
##  7 cycle 6  15600 104478134.
##  8 cycle 7  15600  35192085.
##  9 cycle 8  15600   9343951.
## 10 cycle 9  15600   2148151.
## 11 cycle 10 15600    552926.
## 12 cycle 11 15600    146176.
## 13 cycle 12 15600     50844.
## 14 cycle 13 15600     19066.
## 15 cycle 14 15600         0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 252404782.
##  4 cycle 3  15600 286176034.
##  5 cycle 4  15600 249845450.
##  6 cycle 5  15600 172758732.
##  7 cycle 6  15600 104920305.
##  8 cycle 7  15600  36389500.
##  9 cycle 8  15600   9606271.
## 10 cycle 9  15600   2383304.
## 11 cycle 10 15600    571993.
## 12 cycle 11 15600    133465.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600     19066.
## 15 cycle 14 15600         0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285179122.
##  3 cycle 2  15600 253535839.
##  4 cycle 3  15600 287122219.
##  5 cycle 4  15600 249567508.
##  6 cycle 5  15600 173223742.
##  7 cycle 6  15600 104657806.
##  8 cycle 7  15600  35152546.
##  9 cycle 8  15600   9687374.
## 10 cycle 9  15600   2472280.
## 11 cycle 10 15600    552926.
## 12 cycle 11 15600    158887.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 251448070.
##  4 cycle 3  15600 285188311.
##  5 cycle 4  15600 246703944.
##  6 cycle 5  15600 172953131.
##  7 cycle 6  15600 103418254.
##  8 cycle 7  15600  36542699.
##  9 cycle 8  15600   9791037.
## 10 cycle 9  15600   2650234.
## 11 cycle 10 15600    654614.
## 12 cycle 11 15600    158887.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 254890172.
##  4 cycle 3  15600 287977337.
##  5 cycle 4  15600 248342396.
##  6 cycle 5  15600 172373726.
##  7 cycle 6  15600 103126575.
##  8 cycle 7  15600  35594221.
##  9 cycle 8  15600   9417745.
## 10 cycle 9  15600   2256194.
## 11 cycle 10 15600    451239.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285141512.
##  3 cycle 2  15600 252356832.
##  4 cycle 3  15600 287680605.
##  5 cycle 4  15600 250457340.
##  6 cycle 5  15600 173533788.
##  7 cycle 6  15600 104656258.
##  8 cycle 7  15600  35852810.
##  9 cycle 8  15600  10020798.
## 10 cycle 9  15600   2548546.
## 11 cycle 10 15600    552926.
## 12 cycle 11 15600    139820.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 252932716.
##  4 cycle 3  15600 286311812.
##  5 cycle 4  15600 249690142.
##  6 cycle 5  15600 174538132.
##  7 cycle 6  15600 104291155.
##  8 cycle 7  15600  35281221.
##  9 cycle 8  15600   9203795.
## 10 cycle 9  15600   2338815.
## 11 cycle 10 15600    578348.
## 12 cycle 11 15600    146176.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 283449054.
##  3 cycle 2  15600 248132202.
##  4 cycle 3  15600 284072571.
##  5 cycle 4  15600 246771668.
##  6 cycle 5  15600 171706702.
##  7 cycle 6  15600 103183357.
##  8 cycle 7  15600  35358051.
##  9 cycle 8  15600   9833256.
## 10 cycle 9  15600   2415081.
## 11 cycle 10 15600    584704.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 251664659.
##  4 cycle 3  15600 285617017.
##  5 cycle 4  15600 247777037.
##  6 cycle 5  15600 171693991.
##  7 cycle 6  15600 102579185.
##  8 cycle 7  15600  34773380.
##  9 cycle 8  15600   9075605.
## 10 cycle 9  15600   2243483.
## 11 cycle 10 15600    508438.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 254642760.
##  4 cycle 3  15600 288273419.
##  5 cycle 4  15600 253168597.
##  6 cycle 5  15600 174623882.
##  7 cycle 6  15600 103606790.
##  8 cycle 7  15600  34959518.
##  9 cycle 8  15600   9619132.
## 10 cycle 9  15600   2484991.
## 11 cycle 10 15600    483016.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284332893.
##  3 cycle 2  15600 253108694.
##  4 cycle 3  15600 287342775.
##  5 cycle 4  15600 248943091.
##  6 cycle 5  15600 170681979.
##  7 cycle 6  15600 102107844.
##  8 cycle 7  15600  35265745.
##  9 cycle 8  15600   9475902.
## 10 cycle 9  15600   2370593.
## 11 cycle 10 15600    406751.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285385978.
##  3 cycle 2  15600 254403990.
##  4 cycle 3  15600 287477693.
##  5 cycle 4  15600 251340833.
##  6 cycle 5  15600 173265013.
##  7 cycle 6  15600 104140095.
##  8 cycle 7  15600  34975888.
##  9 cycle 8  15600   9489149.
## 10 cycle 9  15600   2294327.
## 11 cycle 10 15600    451239.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 253005620.
##  4 cycle 3  15600 287299956.
##  5 cycle 4  15600 250852724.
##  6 cycle 5  15600 172496372.
##  7 cycle 6  15600 102920339.
##  8 cycle 7  15600  35564018.
##  9 cycle 8  15600   9971445.
## 10 cycle 9  15600   2389659.
## 11 cycle 10 15600    610126.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 254237959.
##  4 cycle 3  15600 286806200.
##  5 cycle 4  15600 250050762.
##  6 cycle 5  15600 174242735.
##  7 cycle 6  15600 102679712.
##  8 cycle 7  15600  35284535.
##  9 cycle 8  15600   9698741.
## 10 cycle 9  15600   2243483.
## 11 cycle 10 15600    533860.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 253872954.
##  4 cycle 3  15600 286265017.
##  5 cycle 4  15600 250573399.
##  6 cycle 5  15600 172980405.
##  7 cycle 6  15600 103709384.
##  8 cycle 7  15600  36212093.
##  9 cycle 8  15600   9559569.
## 10 cycle 9  15600   2389659.
## 11 cycle 10 15600    584704.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 254594810.
##  4 cycle 3  15600 286708404.
##  5 cycle 4  15600 249600704.
##  6 cycle 5  15600 171711779.
##  7 cycle 6  15600 101598998.
##  8 cycle 7  15600  35212229.
##  9 cycle 8  15600   9764417.
## 10 cycle 9  15600   2243483.
## 11 cycle 10 15600    514794.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285028682.
##  3 cycle 2  15600 252752173.
##  4 cycle 3  15600 287259240.
##  5 cycle 4  15600 250639269.
##  6 cycle 5  15600 173978454.
##  7 cycle 6  15600 102850539.
##  8 cycle 7  15600  35946904.
##  9 cycle 8  15600   9118036.
## 10 cycle 9  15600   2148151.
## 11 cycle 10 15600    521149.
## 12 cycle 11 15600    171598.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600     25422.
## 15 cycle 14 15600      6355.
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 251057787.
##  4 cycle 3  15600 283701766.
##  5 cycle 4  15600 250148967.
##  6 cycle 5  15600 175005715.
##  7 cycle 6  15600 105788534.
##  8 cycle 7  15600  36974605.
##  9 cycle 8  15600  10160356.
## 10 cycle 9  15600   2370593.
## 11 cycle 10 15600    483016.
## 12 cycle 11 15600     95332.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 250203009.
##  4 cycle 3  15600 284724547.
##  5 cycle 4  15600 249603418.
##  6 cycle 5  15600 173430216.
##  7 cycle 6  15600 102671386.
##  8 cycle 7  15600  34972285.
##  9 cycle 8  15600   9297162.
## 10 cycle 9  15600   2275261.
## 11 cycle 10 15600    463950.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 253592432.
##  4 cycle 3  15600 285840077.
##  5 cycle 4  15600 251266636.
##  6 cycle 5  15600 175842368.
##  7 cycle 6  15600 104738018.
##  8 cycle 7  15600  36607484.
##  9 cycle 8  15600   9934143.
## 10 cycle 9  15600   2370593.
## 11 cycle 10 15600    470305.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 253098747.
##  4 cycle 3  15600 285556994.
##  5 cycle 4  15600 252967382.
##  6 cycle 5  15600 175739465.
##  7 cycle 6  15600 104611984.
##  8 cycle 7  15600  37252128.
##  9 cycle 8  15600  10438102.
## 10 cycle 9  15600   2618456.
## 11 cycle 10 15600    616481.
## 12 cycle 11 15600    152531.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 251628290.
##  4 cycle 3  15600 286467930.
##  5 cycle 4  15600 249110352.
##  6 cycle 5  15600 173456873.
##  7 cycle 6  15600 103388546.
##  8 cycle 7  15600  35558140.
##  9 cycle 8  15600   9400526.
## 10 cycle 9  15600   2453214.
## 11 cycle 10 15600    508438.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 254241220.
##  4 cycle 3  15600 286835160.
##  5 cycle 4  15600 248639102.
##  6 cycle 5  15600 173949876.
##  7 cycle 6  15600 103035970.
##  8 cycle 7  15600  35199606.
##  9 cycle 8  15600   9172345.
## 10 cycle 9  15600   2415081.
## 11 cycle 10 15600    451239.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284389309.
##  3 cycle 2  15600 252974632.
##  4 cycle 3  15600 285634642.
##  5 cycle 4  15600 249020340.
##  6 cycle 5  15600 172718679.
##  7 cycle 6  15600 104253650.
##  8 cycle 7  15600  36413102.
##  9 cycle 8  15600   9957687.
## 10 cycle 9  15600   2389659.
## 11 cycle 10 15600    502083.
## 12 cycle 11 15600    146176.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 254035559.
##  4 cycle 3  15600 288772642.
##  5 cycle 4  15600 249296091.
##  6 cycle 5  15600 171951891.
##  7 cycle 6  15600 102941683.
##  8 cycle 7  15600  35900304.
##  9 cycle 8  15600   9446033.
## 10 cycle 9  15600   2440503.
## 11 cycle 10 15600    540216.
## 12 cycle 11 15600    133465.
## 13 cycle 12 15600     44488.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 254618458.
##  4 cycle 3  15600 285363315.
##  5 cycle 4  15600 248862083.
##  6 cycle 5  15600 172231439.
##  7 cycle 6  15600 103302113.
##  8 cycle 7  15600  36910570.
##  9 cycle 8  15600  10356402.
## 10 cycle 9  15600   2593035.
## 11 cycle 10 15600    495727.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 251275517.
##  4 cycle 3  15600 287195031.
##  5 cycle 4  15600 250724088.
##  6 cycle 5  15600 171361748.
##  7 cycle 6  15600 102839608.
##  8 cycle 7  15600  35168627.
##  9 cycle 8  15600   9488165.
## 10 cycle 9  15600   2294327.
## 11 cycle 10 15600    463950.
## 12 cycle 11 15600     95332.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     19066.
## 15 cycle 14 15600         0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285348368.
##  3 cycle 2  15600 253388076.
##  4 cycle 3  15600 288758573.
##  5 cycle 4  15600 253514499.
##  6 cycle 5  15600 174878008.
##  7 cycle 6  15600 102686490.
##  8 cycle 7  15600  35271163.
##  9 cycle 8  15600   9896368.
## 10 cycle 9  15600   2402370.
## 11 cycle 10 15600    559282.
## 12 cycle 11 15600    158887.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0
m.M <- m.C <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_females

The same reasoning is applied to female patients:

#Females
Probs <- function(state){
  return(transition_prob_f_alt[[state]])
}
Costs <- function(state) {
  return(transition_costs_f[[state]])
}
# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_f_alt <- transition_prob_f_alt %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_alt <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M_alt[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_f_alt[[10]][[current_state]]), 1, prob = transition_prob_f_alt[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_f_alt[[t]][[current_state]]), 1, prob = transition_prob_f_alt[[t]][[current_state]])
         }
        m.M_alt[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M_alt)
}

# Init m.M #repeat it!!!!
model_results_f_alt <- list()
for(i in 1:n.sim) {
m.M_alt <- m.C_alt <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M_alt[, 1] <- v.M_1_females
# Microsim loop
model_results_f_alt[[i]] <- loop_microsim_alt(n.t)
print(i)
}  
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
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## [1] 94
## [1] 95
## [1] 96
## [1] 97
## [1] 98
## [1] 99
## [1] 100
# repeat it!!!

#Results of the median simulation, the 50th
model_results_f_alt[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 2   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 3   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 5   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 7   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 8   "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 9   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 11  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 15  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 17  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 18  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 19  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 22  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 26  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 30  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 31  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 32  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 33  "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 38  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 43  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 48  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 53  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 55  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 60  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 61  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 64  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 65  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 66  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 70  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 71  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 72  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 74  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 76  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 80  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 81  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 82  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 85  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 87  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 90  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 92  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 94  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 96  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 97  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 98  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 101 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 102 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 104 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 105 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"  
## ind 107 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"  
## ind 108 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 112 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 116 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 117 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 120 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 124 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 125 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 128 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 129 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 132 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 133 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 135 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 137 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 139 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 141 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 143 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 145 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"  
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 152 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 155 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 156 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 164 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 165 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 166 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 168 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 173 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 174 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 178 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 179 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 182 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 188 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 190 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 196 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 197 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 198 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 203 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 204 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 206 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 207 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 208 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 209 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 211 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 213 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 214 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 217 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 220 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 224 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 228 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 230 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 233 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 235 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 238 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 239 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 241 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 243 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 244 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 250 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 255 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 258 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 259 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 267 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 268 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 269 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 270 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 272 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 273 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 278 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 279 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 281 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 287 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 294 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 296 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 299 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 2   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 20  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 21  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 22  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 23  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 24  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 25  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 26  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 27  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 28  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 29  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 30  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 31  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 32  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 33  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 34  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 35  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 36  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 37  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 38  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 39  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 40  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 41  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 42  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 43  "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 44  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 45  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 46  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 47  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 48  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 49  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 50  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 51  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 52  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 53  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 54  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 55  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 56  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 57  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 58  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 59  "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "MPD"    "D"     
## ind 60  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 61  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 62  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 63  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 64  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 65  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 66  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 67  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 68  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 69  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 70  "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 71  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 72  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 73  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 74  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 75  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 76  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 77  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 78  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 79  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 80  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 81  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 82  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 83  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 84  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 85  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 86  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 87  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 88  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 89  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 90  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 91  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 92  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 93  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 94  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 95  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 96  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 97  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 98  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 99  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 100 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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df_m.M_alt <- model_results_f_alt[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M_alt %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 10400       1
## 
## [[2]]
##  cycle 1     n percent
##        D   143 0.01375
##      MPD 10257 0.98625
## 
## [[3]]
##  cycle 2    n    percent
##      APD  312 0.03000000
##        D  564 0.05423077
##      MPD 9524 0.91576923
## 
## [[4]]
##  cycle 3    n    percent
##      APD  739 0.07105769
##        D  959 0.09221154
##      MPD 8702 0.83673077
## 
## [[5]]
##  cycle 4    n    percent
##      APD  938 0.09019231
##        D 1869 0.17971154
##      MPD 7593 0.73009615
## 
## [[6]]
##  cycle 5    n   percent
##      APD 1055 0.1014423
##        D 3013 0.2897115
##      MPD 6332 0.6088462
## 
## [[7]]
##  cycle 6    n    percent
##      APD  925 0.08894231
##        D 4585 0.44086538
##      MPD 4890 0.47019231
## 
## [[8]]
##  cycle 7    n    percent
##      APD  601 0.05778846
##        D 6463 0.62144231
##      MPD 3336 0.32076923
## 
## [[9]]
##  cycle 8    n    percent
##      APD  275 0.02644231
##        D 8225 0.79086538
##      MPD 1900 0.18269231
## 
## [[10]]
##  cycle 9    n    percent
##      APD   78 0.00750000
##        D 9532 0.91653846
##      MPD  790 0.07596154
## 
## [[11]]
##  cycle 10     n     percent
##       APD    24 0.002307692
##         D 10095 0.970673077
##       MPD   281 0.027019231
## 
## [[12]]
##  cycle 11     n      percent
##       APD     6 0.0005769231
##         D 10292 0.9896153846
##       MPD   102 0.0098076923
## 
## [[13]]
##  cycle 12     n      percent
##       APD     3 0.0002884615
##         D 10355 0.9956730769
##       MPD    42 0.0040384615
## 
## [[14]]
##  cycle 13     n     percent
##         D 10384 0.998461538
##       MPD    16 0.001538462
## 
## [[15]]
##  cycle 14     n      percent
##         D 10393 0.9993269231
##       MPD     7 0.0006730769
#Transition costs
transition_costs_f_alt <-
  transition_costs_f_alt %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
    "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")

final_cost_f_alt <- map(
    model_results_f_alt,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_f_alt
      )
  )
 

final_cost_f2_alt <-
  map(
    final_cost_f_alt,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
    filter(cycle != "cycle 15")
  )
final_cost_f2_alt
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 179507672.
##  4 cycle 3  10400 170151745.
##  5 cycle 4  10400 198484281.
##  6 cycle 5  10400 160694696.
##  7 cycle 6  10400 134000463.
##  8 cycle 7  10400  61785443.
##  9 cycle 8  10400  14035813.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 177274255.
##  4 cycle 3  10400 169090903.
##  5 cycle 4  10400 195417254.
##  6 cycle 5  10400 158675876.
##  7 cycle 6  10400 131589524.
##  8 cycle 7  10400  62479016.
##  9 cycle 8  10400  15134717.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 179553206.
##  4 cycle 3  10400 169923235.
##  5 cycle 4  10400 196656312.
##  6 cycle 5  10400 160092588.
##  7 cycle 6  10400 131336168.
##  8 cycle 7  10400  61211558.
##  9 cycle 8  10400  14274411.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 179338890.
##  4 cycle 3  10400 170290115.
##  5 cycle 4  10400 196533600.
##  6 cycle 5  10400 159400827.
##  7 cycle 6  10400 133182018.
##  8 cycle 7  10400  61575070.
##  9 cycle 8  10400  13669154.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    116935.
## 12 cycle 11 10400         0 
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 178617218.
##  4 cycle 3  10400 170125387.
##  5 cycle 4  10400 195301032.
##  6 cycle 5  10400 159166369.
##  7 cycle 6  10400 129763714.
##  8 cycle 7  10400  60914953.
##  9 cycle 8  10400  14568862.
## 10 cycle 9  10400   1087497.
## 11 cycle 10 10400    385886.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 179486827.
##  4 cycle 3  10400 169153106.
##  5 cycle 4  10400 197986738.
##  6 cycle 5  10400 160804180.
##  7 cycle 6  10400 133366172.
##  8 cycle 7  10400  62426978.
##  9 cycle 8  10400  14249017.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 179091385.
##  4 cycle 3  10400 170036567.
##  5 cycle 4  10400 196508402.
##  6 cycle 5  10400 159847783.
##  7 cycle 6  10400 131565747.
##  8 cycle 7  10400  61554254.
##  9 cycle 8  10400  14938144.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249629414.
##  3 cycle 2  10400 180705532.
##  4 cycle 3  10400 170525475.
##  5 cycle 4  10400 196815612.
##  6 cycle 5  10400 160082673.
##  7 cycle 6  10400 133516947.
##  8 cycle 7  10400  61704419.
##  9 cycle 8  10400  14751606.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 178776083.
##  4 cycle 3  10400 167866655.
##  5 cycle 4  10400 196165604.
##  6 cycle 5  10400 160187416.
##  7 cycle 6  10400 131422446.
##  8 cycle 7  10400  60906777.
##  9 cycle 8  10400  14246394.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250506674.
##  3 cycle 2  10400 180119858.
##  4 cycle 3  10400 169767998.
##  5 cycle 4  10400 198530604.
##  6 cycle 5  10400 161798060.
##  7 cycle 6  10400 134934502.
##  8 cycle 7  10400  63852025.
##  9 cycle 8  10400  14689334.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 179727249.
##  4 cycle 3  10400 170094023.
##  5 cycle 4  10400 197426442.
##  6 cycle 5  10400 159128438.
##  7 cycle 6  10400 131164579.
##  8 cycle 7  10400  60981857.
##  9 cycle 8  10400  13803455.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 178774666.
##  4 cycle 3  10400 169517875.
##  5 cycle 4  10400 196760349.
##  6 cycle 5  10400 159053009.
##  7 cycle 6  10400 132453201.
##  8 cycle 7  10400  62797917.
##  9 cycle 8  10400  14839451.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 179333223.
##  4 cycle 3  10400 168504739.
##  5 cycle 4  10400 195738927.
##  6 cycle 5  10400 158181956.
##  7 cycle 6  10400 130861287.
##  8 cycle 7  10400  60655516.
##  9 cycle 8  10400  14372469.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249434467.
##  3 cycle 2  10400 178484460.
##  4 cycle 3  10400 170022070.
##  5 cycle 4  10400 197481706.
##  6 cycle 5  10400 159255158.
##  7 cycle 6  10400 133455413.
##  8 cycle 7  10400  61538643.
##  9 cycle 8  10400  15522975.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 179975765.
##  4 cycle 3  10400 170218952.
##  5 cycle 4  10400 197492268.
##  6 cycle 5  10400 158618996.
##  7 cycle 6  10400 131457240.
##  8 cycle 7  10400  61245009.
##  9 cycle 8  10400  14654720.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 179601168.
##  4 cycle 3  10400 171515154.
##  5 cycle 4  10400 197358649.
##  6 cycle 5  10400 160973573.
##  7 cycle 6  10400 132571310.
##  8 cycle 7  10400  64065374.
##  9 cycle 8  10400  15839025.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 178869985.
##  4 cycle 3  10400 168573002.
##  5 cycle 4  10400 195762330.
##  6 cycle 5  10400 159210756.
##  7 cycle 6  10400 133985128.
##  8 cycle 7  10400  62715410.
##  9 cycle 8  10400  16209757.
## 10 cycle 9  10400   1157658.
## 11 cycle 10 10400    362499.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400         0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 178099341.
##  4 cycle 3  10400 169996243.
##  5 cycle 4  10400 198408859.
##  6 cycle 5  10400 159901652.
##  7 cycle 6  10400 130611281.
##  8 cycle 7  10400  61158033.
##  9 cycle 8  10400  14326649.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249726887.
##  3 cycle 2  10400 181418297.
##  4 cycle 3  10400 170156750.
##  5 cycle 4  10400 196256938.
##  6 cycle 5  10400 158010850.
##  7 cycle 6  10400 131617424.
##  8 cycle 7  10400  61950472.
##  9 cycle 8  10400  14247388.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 178349274.
##  4 cycle 3  10400 170354952.
##  5 cycle 4  10400 196325871.
##  6 cycle 5  10400 159477555.
##  7 cycle 6  10400 132951858.
##  8 cycle 7  10400  61364691.
##  9 cycle 8  10400  14101881.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249751255.
##  3 cycle 2  10400 178332273.
##  4 cycle 3  10400 169778539.
##  5 cycle 4  10400 197223753.
##  6 cycle 5  10400 159764600.
##  7 cycle 6  10400 134657950.
##  8 cycle 7  10400  63539807.
##  9 cycle 8  10400  15488361.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 179305701.
##  4 cycle 3  10400 170232393.
##  5 cycle 4  10400 197972586.
##  6 cycle 5  10400 159796492.
##  7 cycle 6  10400 132870865.
##  8 cycle 7  10400  62956258.
##  9 cycle 8  10400  14779622.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 178033972.
##  4 cycle 3  10400 169827034.
##  5 cycle 4  10400 195229027.
##  6 cycle 5  10400 156479912.
##  7 cycle 6  10400 129268213.
##  8 cycle 7  10400  60882244.
##  9 cycle 8  10400  14130533.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 179121741.
##  4 cycle 3  10400 168380601.
##  5 cycle 4  10400 196549685.
##  6 cycle 5  10400 158533219.
##  7 cycle 6  10400 131768974.
##  8 cycle 7  10400  61949728.
##  9 cycle 8  10400  14277212.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 177353990.
##  4 cycle 3  10400 169021586.
##  5 cycle 4  10400 195613281.
##  6 cycle 5  10400 157845334.
##  7 cycle 6  10400 132177616.
##  8 cycle 7  10400  62137803.
##  9 cycle 8  10400  14016202.
## 10 cycle 9  10400    713305.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 177800634.
##  4 cycle 3  10400 169711332.
##  5 cycle 4  10400 197557300.
##  6 cycle 5  10400 159730545.
##  7 cycle 6  10400 133248256.
##  8 cycle 7  10400  62408392.
##  9 cycle 8  10400  15304624.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 177948570.
##  4 cycle 3  10400 169258002.
##  5 cycle 4  10400 195701371.
##  6 cycle 5  10400 157402271.
##  7 cycle 6  10400 132350172.
##  8 cycle 7  10400  61262110.
##  9 cycle 8  10400  15232775.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 179873363.
##  4 cycle 3  10400 171913397.
##  5 cycle 4  10400 198638680.
##  6 cycle 5  10400 159449106.
##  7 cycle 6  10400 132570536.
##  8 cycle 7  10400  62743653.
##  9 cycle 8  10400  14332252.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 178820607.
##  4 cycle 3  10400 170056334.
##  5 cycle 4  10400 196958482.
##  6 cycle 5  10400 158480215.
##  7 cycle 6  10400 132077550.
##  8 cycle 7  10400  61019772.
##  9 cycle 8  10400  14683551.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 180020289.
##  4 cycle 3  10400 168852908.
##  5 cycle 4  10400 196671603.
##  6 cycle 5  10400 159553833.
##  7 cycle 6  10400 133046191.
##  8 cycle 7  10400  61240547.
##  9 cycle 8  10400  14996164.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249507572.
##  3 cycle 2  10400 178181909.
##  4 cycle 3  10400 168945152.
##  5 cycle 4  10400 194504736.
##  6 cycle 5  10400 156376050.
##  7 cycle 6  10400 130939317.
##  8 cycle 7  10400  60576716.
##  9 cycle 8  10400  13983575.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 179536612.
##  4 cycle 3  10400 169791980.
##  5 cycle 4  10400 194485717.
##  6 cycle 5  10400 157114776.
##  7 cycle 6  10400 131253820.
##  8 cycle 7  10400  61995075.
##  9 cycle 8  10400  14573830.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 179334640.
##  4 cycle 3  10400 169387676.
##  5 cycle 4  10400 197622643.
##  6 cycle 5  10400 160299030.
##  7 cycle 6  10400 133123447.
##  8 cycle 7  10400  62656682.
##  9 cycle 8  10400  15810650.
## 10 cycle 9  10400    993949.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249873097.
##  3 cycle 2  10400 178986555.
##  4 cycle 3  10400 169482821.
##  5 cycle 4  10400 197026759.
##  6 cycle 5  10400 160078797.
##  7 cycle 6  10400 134311616.
##  8 cycle 7  10400  61936347.
##  9 cycle 8  10400  14148694.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 179785128.
##  4 cycle 3  10400 172439995.
##  5 cycle 4  10400 196615994.
##  6 cycle 5  10400 159122000.
##  7 cycle 6  10400 130143427.
##  8 cycle 7  10400  60932798.
##  9 cycle 8  10400  14131347.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 178028305.
##  4 cycle 3  10400 169838630.
##  5 cycle 4  10400 196821135.
##  6 cycle 5  10400 156666107.
##  7 cycle 6  10400 129521956.
##  8 cycle 7  10400  60197599.
##  9 cycle 8  10400  13932788.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    152016.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 179403853.
##  4 cycle 3  10400 171097143.
##  5 cycle 4  10400 197499103.
##  6 cycle 5  10400 160346877.
##  7 cycle 6  10400 133917728.
##  8 cycle 7  10400  63009783.
##  9 cycle 8  10400  15005384.
## 10 cycle 9  10400   1145965.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 179985277.
##  4 cycle 3  10400 171131142.
##  5 cycle 4  10400 196250276.
##  6 cycle 5  10400 159405153.
##  7 cycle 6  10400 131197377.
##  8 cycle 7  10400  60369319.
##  9 cycle 8  10400  14052623.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    362499.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 180171664.
##  4 cycle 3  10400 170216051.
##  5 cycle 4  10400 194854852.
##  6 cycle 5  10400 156583790.
##  7 cycle 6  10400 130441240.
##  8 cycle 7  10400  61303739.
##  9 cycle 8  10400  13910553.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 180984811.
##  4 cycle 3  10400 170551568.
##  5 cycle 4  10400 197632066.
##  6 cycle 5  10400 160911502.
##  7 cycle 6  10400 133358117.
##  8 cycle 7  10400  62541456.
##  9 cycle 8  10400  14075216.
## 10 cycle 9  10400    654837.
## 11 cycle 10 10400    116935.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 180301995.
##  4 cycle 3  10400 169992552.
##  5 cycle 4  10400 197238216.
##  6 cycle 5  10400 158887509.
##  7 cycle 6  10400 132910170.
##  8 cycle 7  10400  59859362.
##  9 cycle 8  10400  13978608.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 179538028.
##  4 cycle 3  10400 168649700.
##  5 cycle 4  10400 196967112.
##  6 cycle 5  10400 160399897.
##  7 cycle 6  10400 134538232.
##  8 cycle 7  10400  63041749.
##  9 cycle 8  10400  15219760.
## 10 cycle 9  10400   1099191.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249361362.
##  3 cycle 2  10400 176359112.
##  4 cycle 3  10400 167990003.
##  5 cycle 4  10400 196218106.
##  6 cycle 5  10400 158870257.
##  7 cycle 6  10400 131986374.
##  8 cycle 7  10400  61452411.
##  9 cycle 8  10400  14626704.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250555411.
##  3 cycle 2  10400 179496744.
##  4 cycle 3  10400 169732679.
##  5 cycle 4  10400 195916418.
##  6 cycle 5  10400 157999637.
##  7 cycle 6  10400 130479772.
##  8 cycle 7  10400  61302998.
##  9 cycle 8  10400  13851718.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    385886.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 178773249.
##  4 cycle 3  10400 170167821.
##  5 cycle 4  10400 196325215.
##  6 cycle 5  10400 158139716.
##  7 cycle 6  10400 131230818.
##  8 cycle 7  10400  61292587.
##  9 cycle 8  10400  15103263.
## 10 cycle 9  10400   1239513.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 179514350.
##  4 cycle 3  10400 169650975.
##  5 cycle 4  10400 195313389.
##  6 cycle 5  10400 158186697.
##  7 cycle 6  10400 131488298.
##  8 cycle 7  10400  60758100.
##  9 cycle 8  10400  14362077.
## 10 cycle 9  10400   1040723.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 177738098.
##  4 cycle 3  10400 168785170.
##  5 cycle 4  10400 196153902.
##  6 cycle 5  10400 157904809.
##  7 cycle 6  10400 131382754.
##  8 cycle 7  10400  61604803.
##  9 cycle 8  10400  13863104.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    362499.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 179496338.
##  4 cycle 3  10400 169727408.
##  5 cycle 4  10400 197275116.
##  6 cycle 5  10400 160547729.
##  7 cycle 6  10400 132815644.
##  8 cycle 7  10400  61384019.
##  9 cycle 8  10400  14260581.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 180169236.
##  4 cycle 3  10400 171193344.
##  5 cycle 4  10400 197923985.
##  6 cycle 5  10400 162071763.
##  7 cycle 6  10400 133867986.
##  8 cycle 7  10400  62809071.
##  9 cycle 8  10400  14279021.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 178317501.
##  4 cycle 3  10400 170226333.
##  5 cycle 4  10400 197857020.
##  6 cycle 5  10400 161458010.
##  7 cycle 6  10400 134790427.
##  8 cycle 7  10400  63156228.
##  9 cycle 8  10400  15065848.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 178665586.
##  4 cycle 3  10400 169767998.
##  5 cycle 4  10400 196327010.
##  6 cycle 5  10400 157441051.
##  7 cycle 6  10400 131175984.
##  8 cycle 7  10400  61972029.
##  9 cycle 8  10400  14926937.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 179953910.
##  4 cycle 3  10400 170293805.
##  5 cycle 4  10400 196752064.
##  6 cycle 5  10400 159536581.
##  7 cycle 6  10400 132830012.
##  8 cycle 7  10400  62562266.
##  9 cycle 8  10400  14325199.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249775624.
##  3 cycle 2  10400 178989993.
##  4 cycle 3  10400 169691824.
##  5 cycle 4  10400 194677188.
##  6 cycle 5  10400 156485102.
##  7 cycle 6  10400 130695177.
##  8 cycle 7  10400  60714988.
##  9 cycle 8  10400  13985025.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249385730.
##  3 cycle 2  10400 178203764.
##  4 cycle 3  10400 169181303.
##  5 cycle 4  10400 196078308.
##  6 cycle 5  10400 159202570.
##  7 cycle 6  10400 133455026.
##  8 cycle 7  10400  62680466.
##  9 cycle 8  10400  15364453.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 181317718.
##  4 cycle 3  10400 169005244.
##  5 cycle 4  10400 196870703.
##  6 cycle 5  10400 159088361.
##  7 cycle 6  10400 131107749.
##  8 cycle 7  10400  60179759.
##  9 cycle 8  10400  14215397.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 178280061.
##  4 cycle 3  10400 168779899.
##  5 cycle 4  10400 197087372.
##  6 cycle 5  10400 160021484.
##  7 cycle 6  10400 132626337.
##  8 cycle 7  10400  62634381.
##  9 cycle 8  10400  15673727.
## 10 cycle 9  10400   1110884.
## 11 cycle 10 10400    374193.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 179160191.
##  4 cycle 3  10400 170735798.
##  5 cycle 4  10400 197657092.
##  6 cycle 5  10400 159912432.
##  7 cycle 6  10400 131523479.
##  8 cycle 7  10400  61697728.
##  9 cycle 8  10400  15256817.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250457938.
##  3 cycle 2  10400 178411809.
##  4 cycle 3  10400 169253787.
##  5 cycle 4  10400 197630444.
##  6 cycle 5  10400 161776083.
##  7 cycle 6  10400 134088930.
##  8 cycle 7  10400  63681793.
##  9 cycle 8  10400  15275435.
## 10 cycle 9  10400   1052417.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 179096041.
##  4 cycle 3  10400 170093233.
##  5 cycle 4  10400 197466587.
##  6 cycle 5  10400 158725886.
##  7 cycle 6  10400 130001347.
##  8 cycle 7  10400  60836156.
##  9 cycle 8  10400  15289622.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 180094763.
##  4 cycle 3  10400 171070260.
##  5 cycle 4  10400 198080834.
##  6 cycle 5  10400 158810366.
##  7 cycle 6  10400 131367999.
##  8 cycle 7  10400  61051734.
##  9 cycle 8  10400  13826504.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 178842462.
##  4 cycle 3  10400 169513395.
##  5 cycle 4  10400 196448582.
##  6 cycle 5  10400 160762389.
##  7 cycle 6  10400 134126433.
##  8 cycle 7  10400  62774879.
##  9 cycle 8  10400  15403318.
## 10 cycle 9  10400   1134271.
## 11 cycle 10 10400    374193.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 178336929.
##  4 cycle 3  10400 168920115.
##  5 cycle 4  10400 197959918.
##  6 cycle 5  10400 160235679.
##  7 cycle 6  10400 131433657.
##  8 cycle 7  10400  60914206.
##  9 cycle 8  10400  13734944.
## 10 cycle 9  10400    993949.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250652885.
##  3 cycle 2  10400 177835240.
##  4 cycle 3  10400 167993428.
##  5 cycle 4  10400 195853527.
##  6 cycle 5  10400 158738796.
##  7 cycle 6  10400 133391944.
##  8 cycle 7  10400  63035053.
##  9 cycle 8  10400  15925876.
## 10 cycle 9  10400   1099191.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 178216515.
##  4 cycle 3  10400 169903467.
##  5 cycle 4  10400 198205997.
##  6 cycle 5  10400 159820199.
##  7 cycle 6  10400 133513404.
##  8 cycle 7  10400  63131695.
##  9 cycle 8  10400  15195539.
## 10 cycle 9  10400   1145965.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 179707821.
##  4 cycle 3  10400 170875224.
##  5 cycle 4  10400 198032406.
##  6 cycle 5  10400 159785279.
##  7 cycle 6  10400 135842576.
##  8 cycle 7  10400  63827492.
##  9 cycle 8  10400  16005773.
## 10 cycle 9  10400   1075804.
## 11 cycle 10 10400    362499.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 177210303.
##  4 cycle 3  10400 167298678.
##  5 cycle 4  10400 197437350.
##  6 cycle 5  10400 159935690.
##  7 cycle 6  10400 133067259.
##  8 cycle 7  10400  63553927.
##  9 cycle 8  10400  15895058.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 178823034.
##  4 cycle 3  10400 168635994.
##  5 cycle 4  10400 196986269.
##  6 cycle 5  10400 157227721.
##  7 cycle 6  10400 129833883.
##  8 cycle 7  10400  60552188.
##  9 cycle 8  10400  14114895.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 178505305.
##  4 cycle 3  10400 169853127.
##  5 cycle 4  10400 196345201.
##  6 cycle 5  10400 157869490.
##  7 cycle 6  10400 130554645.
##  8 cycle 7  10400  61004902.
##  9 cycle 8  10400  14112908.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 179554217.
##  4 cycle 3  10400 170335710.
##  5 cycle 4  10400 198197712.
##  6 cycle 5  10400 160545999.
##  7 cycle 6  10400 133859931.
##  8 cycle 7  10400  62305061.
##  9 cycle 8  10400  15119079.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     23387.
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 178479199.
##  4 cycle 3  10400 169823343.
##  5 cycle 4  10400 196720515.
##  6 cycle 5  10400 158019468.
##  7 cycle 6  10400 131444675.
##  8 cycle 7  10400  61636766.
##  9 cycle 8  10400  14487793.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 178299895.
##  4 cycle 3  10400 170471975.
##  5 cycle 4  10400 197453919.
##  6 cycle 5  10400 156981601.
##  7 cycle 6  10400 132758040.
##  8 cycle 7  10400  63136159.
##  9 cycle 8  10400  15404132.
## 10 cycle 9  10400   1216126.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     23387.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250482306.
##  3 cycle 2  10400 177999365.
##  4 cycle 3  10400 170024706.
##  5 cycle 4  10400 196315619.
##  6 cycle 5  10400 159159049.
##  7 cycle 6  10400 131528183.
##  8 cycle 7  10400  61308201.
##  9 cycle 8  10400  14527831.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 178228453.
##  4 cycle 3  10400 168902717.
##  5 cycle 4  10400 194891096.
##  6 cycle 5  10400 156398892.
##  7 cycle 6  10400 131497319.
##  8 cycle 7  10400  62327368.
##  9 cycle 8  10400  15496229.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 179706404.
##  4 cycle 3  10400 170870743.
##  5 cycle 4  10400 196967422.
##  6 cycle 5  10400 158729329.
##  7 cycle 6  10400 132042176.
##  8 cycle 7  10400  63400055.
##  9 cycle 8  10400  14731994.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 178627136.
##  4 cycle 3  10400 170624311.
##  5 cycle 4  10400 197505765.
##  6 cycle 5  10400 159389614.
##  7 cycle 6  10400 133676164.
##  8 cycle 7  10400  62329594.
##  9 cycle 8  10400  14937151.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    362499.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 178127874.
##  4 cycle 3  10400 168922750.
##  5 cycle 4  10400 194745465.
##  6 cycle 5  10400 156498878.
##  7 cycle 6  10400 129575048.
##  8 cycle 7  10400  60827230.
##  9 cycle 8  10400  14112273.
## 10 cycle 9  10400    701611.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 178212265.
##  4 cycle 3  10400 168131009.
##  5 cycle 4  10400 196724588.
##  6 cycle 5  10400 158744835.
##  7 cycle 6  10400 133738859.
##  8 cycle 7  10400  62590520.
##  9 cycle 8  10400  15190929.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250262991.
##  3 cycle 2  10400 179052528.
##  4 cycle 3  10400 169601424.
##  5 cycle 4  10400 196521070.
##  6 cycle 5  10400 158565127.
##  7 cycle 6  10400 132656814.
##  8 cycle 7  10400  61012337.
##  9 cycle 8  10400  14761640.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 179323712.
##  4 cycle 3  10400 169464900.
##  5 cycle 4  10400 197193688.
##  6 cycle 5  10400 159518497.
##  7 cycle 6  10400 134000463.
##  8 cycle 7  10400  62253029.
##  9 cycle 8  10400  14844875.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 179050101.
##  4 cycle 3  10400 170240830.
##  5 cycle 4  10400 197888397.
##  6 cycle 5  10400 160539544.
##  7 cycle 6  10400 132345274.
##  8 cycle 7  10400  61595883.
##  9 cycle 8  10400  14343459.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 177978520.
##  4 cycle 3  10400 168254882.
##  5 cycle 4  10400 196642504.
##  6 cycle 5  10400 158307811.
##  7 cycle 6  10400 132430391.
##  8 cycle 7  10400  62493882.
##  9 cycle 8  10400  15865690.
## 10 cycle 9  10400   1122578.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 179034516.
##  4 cycle 3  10400 169642804.
##  5 cycle 4  10400 197013918.
##  6 cycle 5  10400 158184535.
##  7 cycle 6  10400 131300213.
##  8 cycle 7  10400  61870931.
##  9 cycle 8  10400  14965346.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 178987972.
##  4 cycle 3  10400 169478606.
##  5 cycle 4  10400 197952600.
##  6 cycle 5  10400 161048986.
##  7 cycle 6  10400 133692080.
##  8 cycle 7  10400  63345786.
##  9 cycle 8  10400  14559643.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     23387.
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 179833496.
##  4 cycle 3  10400 171161715.
##  5 cycle 4  10400 196906291.
##  6 cycle 5  10400 158708650.
##  7 cycle 6  10400 132992904.
##  8 cycle 7  10400  61812208.
##  9 cycle 8  10400  15383706.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 179641848.
##  4 cycle 3  10400 170052119.
##  5 cycle 4  10400 198071584.
##  6 cycle 5  10400 159351267.
##  7 cycle 6  10400 132364733.
##  8 cycle 7  10400  61826334.
##  9 cycle 8  10400  15227986.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 178821617.
##  4 cycle 3  10400 169099864.
##  5 cycle 4  10400 196025495.
##  6 cycle 5  10400 157581546.
##  7 cycle 6  10400 133157854.
##  8 cycle 7  10400  62887125.
##  9 cycle 8  10400  14473785.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 179616346.
##  4 cycle 3  10400 169082732.
##  5 cycle 4  10400 196021250.
##  6 cycle 5  10400 157928100.
##  7 cycle 6  10400 130838478.
##  8 cycle 7  10400  61061398.
##  9 cycle 8  10400  14233379.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 181396848.
##  4 cycle 3  10400 169322049.
##  5 cycle 4  10400 196434775.
##  6 cycle 5  10400 157747062.
##  7 cycle 6  10400 129860042.
##  8 cycle 7  10400  61560942.
##  9 cycle 8  10400  14938144.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249483204.
##  3 cycle 2  10400 179525278.
##  4 cycle 3  10400 171317217.
##  5 cycle 4  10400 197444323.
##  6 cycle 5  10400 159824508.
##  7 cycle 6  10400 132380842.
##  8 cycle 7  10400  62446304.
##  9 cycle 8  10400  14393530.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 177968603.
##  4 cycle 3  10400 169646229.
##  5 cycle 4  10400 197483328.
##  6 cycle 5  10400 157928965.
##  7 cycle 6  10400 129689421.
##  8 cycle 7  10400  61233862.
##  9 cycle 8  10400  14987759.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 178099341.
##  4 cycle 3  10400 169109091.
##  5 cycle 4  10400 197903516.
##  6 cycle 5  10400 160795113.
##  7 cycle 6  10400 133238654.
##  8 cycle 7  10400  61907355.
##  9 cycle 8  10400  13702139.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 178533234.
##  4 cycle 3  10400 168537423.
##  5 cycle 4  10400 197867272.
##  6 cycle 5  10400 159403406.
##  7 cycle 6  10400 133247869.
##  8 cycle 7  10400  62716890.
##  9 cycle 8  10400  14697918.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 179326546.
##  4 cycle 3  10400 169806477.
##  5 cycle 4  10400 197676594.
##  6 cycle 5  10400 160216730.
##  7 cycle 6  10400 133911415.
##  8 cycle 7  10400  61821872.
##  9 cycle 8  10400  14858883.
## 10 cycle 9  10400   1145965.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 178849546.
##  4 cycle 3  10400 169344186.
##  5 cycle 4  10400 196274991.
##  6 cycle 5  10400 159192655.
##  7 cycle 6  10400 130850269.
##  8 cycle 7  10400  60452577.
##  9 cycle 8  10400  14276040.
## 10 cycle 9  10400   1017336.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249653782.
##  3 cycle 2  10400 178632396.
##  4 cycle 3  10400 170116426.
##  5 cycle 4  10400 196266051.
##  6 cycle 5  10400 158147469.
##  7 cycle 6  10400 130907680.
##  8 cycle 7  10400  60691196.
##  9 cycle 8  10400  13876119.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 180537156.
##  4 cycle 3  10400 170893411.
##  5 cycle 4  10400 198369854.
##  6 cycle 5  10400 160511511.
##  7 cycle 6  10400 134603116.
##  8 cycle 7  10400  62386829.
##  9 cycle 8  10400  14845233.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 177999772.
##  4 cycle 3  10400 168864504.
##  5 cycle 4  10400 195158818.
##  6 cycle 5  10400 158736649.
##  7 cycle 6  10400 133991248.
##  8 cycle 7  10400  62853669.
##  9 cycle 8  10400  14185116.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     23387.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 178589290.
##  4 cycle 3  10400 168889276.
##  5 cycle 4  10400 196212893.
##  6 cycle 5  10400 157408310.
##  7 cycle 6  10400 130913351.
##  8 cycle 7  10400  61081470.
##  9 cycle 8  10400  14213589.
## 10 cycle 9  10400   1052417.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 179255916.
##  4 cycle 3  10400 170469599.
##  5 cycle 4  10400 196000608.
##  6 cycle 5  10400 157765612.
##  7 cycle 6  10400 130896855.
##  8 cycle 7  10400  60778916.
##  9 cycle 8  10400  15072266.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 179144003.
##  4 cycle 3  10400 169404808.
##  5 cycle 4  10400 197776731.
##  6 cycle 5  10400 158684062.
##  7 cycle 6  10400 133983773.
##  8 cycle 7  10400  63794783.
##  9 cycle 8  10400  15533188.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0

The variability of costs over 30 simulations is observed through a box plot:

#Males
final_cost_m2_alt_combined <- bind_rows(final_cost_m2_alt)

final_cost_m2_alt_combined$cycle <- factor(final_cost_m2_alt_combined$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_m_alt <- ggplot(final_cost_m2_alt_combined, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Males)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m_alt

#Females
final_cost_f2_alt_combined <- bind_rows(final_cost_f2_alt)

final_cost_f2_alt_combined$cycle <- factor(final_cost_f2_alt_combined$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_f_alt <- ggplot(final_cost_f2_alt_combined, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Females)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f_alt

The highest total cost is reached in “cycle 0” due to the additional medical expenses depicted in “Table 36”. Then, a sharp decrease is observed for male patients due to the higher incidence of mortality compared to female patients. Note that, again, total costs tend to drop earlier for male patients, remarking the higher longevity of female patients, and variability is moderate just like in the baseline scenario.

The graphs showcasing costs over cycles are:

#Averaging costs across simulations
#Males
combined_costs_m_alt <- map_df(final_cost_m2_alt, ~ .x)
mean_costs_per_cycle_m_alt <- combined_costs_m_alt %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_m_alt)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0     440865997.
##  2 cycle 1     284876172.
##  3 cycle 2     253154203.
##  4 cycle 3     286747631.
##  5 cycle 4     250059379.
##  6 cycle 5     173233417.
##  7 cycle 6     103261039.
##  8 cycle 7      35654696.
##  9 cycle 8       9685856.
## 10 cycle 9       2382287.
## 11 cycle 10       517526.
## 12 cycle 11       117004.
## 13 cycle 12        26439.
## 14 cycle 13         5656.
## 15 cycle 14          890.
#Females
combined_costs_f_alt <- map_df(final_cost_f2_alt, ~ .x)
mean_costs_per_cycle_f_alt <- combined_costs_f_alt %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_f_alt)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0     261295475.
##  2 cycle 1     249974470.
##  3 cycle 2     179019037.
##  4 cycle 3     169726896.
##  5 cycle 4     196825548.
##  6 cycle 5     159069070.
##  7 cycle 6     132285512.
##  8 cycle 7      61897567.
##  9 cycle 8      14687261.
## 10 cycle 9        936183.
## 11 cycle 10       260532.
## 12 cycle 11        74605.
## 13 cycle 12        21867.
## 14 cycle 13         7835.
## 15 cycle 14         2456.
#Graphs
#Males
graph1_alt <- ggplot(data = mean_costs_per_cycle_m_alt %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "turquoise") +
  ggtitle("Average total costs from microsimulation, alternative scenario (Males)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m_alt$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
  options(scipen=999)
  
#Females
graph2_alt <- ggplot(data = mean_costs_per_cycle_f_alt %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "pink") +
  ggtitle("Average total costs from microsimulation, alternative scenario (Females)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f_alt$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
  options(scipen=999)

graph1_alt

graph2_alt

Let’s compare average total costs across scenarios:

#Males
mean_costs_combined_m <- mean_costs_per_cycle_m %>%
  rename(avg_tot_costs_baseline = avg_tot_costs) %>%
  inner_join(mean_costs_per_cycle_m_alt %>%
               rename(avg_tot_costs_alt = avg_tot_costs),
             by = "cycle") %>%
  mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>% 
  pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
               names_to = "Scenario", values_to = "avg_tot_costs") %>%
  mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative", "extra_cost" = "Extra cost of baseline")) %>% 
  filter(Scenario != "Baseline") %>% 
  mutate(
    Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
  )

graph_combined_m <- ggplot(data = mean_costs_combined_m, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
  geom_col(data = subset(mean_costs_combined_m, Scenario == "Alternative"), fill = "blue", width = 0.4) +
  geom_col(data = subset(mean_costs_combined_m, Scenario == "Extra cost of baseline"),
           aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")), 
           width = 0.4) +
  scale_fill_manual(name = "Gains/losses", values = c("Alternative" = "blue", "Loss" = "red", "Gain" = "green")) +
  ggtitle("Comparison of average total costs of alternative scenario wrt baseline scenario (Males)") +
  xlab("Cycle") +
  ylab("Cost") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
  scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_m$avg_tot_costs), max(mean_costs_combined_m$avg_tot_costs)))
graph_combined_m

#Females
mean_costs_combined_f <- mean_costs_per_cycle_f %>%
  rename(avg_tot_costs_baseline = avg_tot_costs) %>%
  inner_join(mean_costs_per_cycle_f_alt %>%
               rename(avg_tot_costs_alt = avg_tot_costs),
             by = "cycle") %>%
  mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>% 
  pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
               names_to = "Scenario", values_to = "avg_tot_costs") %>%
  mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative", "extra_cost" = "Extra cost of baseline")) %>% 
  filter(Scenario != "Baseline") %>% 
  mutate(
    Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
  )

graph_combined_f <- ggplot(data = mean_costs_combined_f, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
  geom_col(data = subset(mean_costs_combined_f, Scenario == "Alternative"), fill = "pink", width = 0.4) +
  geom_col(data = subset(mean_costs_combined_f, Scenario == "Extra cost of baseline"),
           aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")), 
           width = 0.4) +
  scale_fill_manual(name = "Gains/losses", values = c("Alternative" = "pink", "Loss" = "red", "Gain" = "green")) +
  ggtitle("Comparison of average total costs of alternative scenario wrt baseline scenario (Females)") +
  xlab("Cycle") +
  ylab("Cost") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
  scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_f$avg_tot_costs), max(mean_costs_combined_f$avg_tot_costs)))
graph_combined_f

Note that “cycle 0” is characterized by a huge loss due to the earlier treatment of prodromal patients. Then, “cycle 1” does not have any gain or loss since the probabilities of transitioning from the prodromal state P in “cycle 0” are not modified by any alternative scenario. The following cycles depict moderate gains and significant losses, with the only exception of “cycle 3” of male patients where there is an insignificant gain/loss. The main takeaway is that, from a financial point of view, early detection does not allow to save any money but rather configures as an investment. The reason is that, globally speaking, the higher probability of remaining in the MPD stage implies a loss for the public health insurance since there are more MPD patients to be treated. As well as that, a lower probability of death is associated with a loss since there are less deceased patients who cost 0. On the contrary, a lower probability of transitioning from MPD to APD results in a gain since APD patients cost more than MPD patients on average. However, two variations out of three explain why losses outbalance gains in the above graphs.

Discounted costs are:

discounted_costs_m_alt <-
  map(final_cost_m2_alt, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_m_alt
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       198267812.
##  4 cycle 3  15600       199067495.
##  5 cycle 4  15600       153891698.
##  6 cycle 5  15600        93956450.
##  7 cycle 6  15600        49306128.
##  8 cycle 7  15600        14708501.
##  9 cycle 8  15600         3511802.
## 10 cycle 9  15600          792891.
## 11 cycle 10 15600          283775.
## 12 cycle 11 15600           67255.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       195522023.
##  4 cycle 3  15600       196456151.
##  5 cycle 4  15600       151273702.
##  6 cycle 5  15600        92146173.
##  7 cycle 6  15600        49369450.
##  8 cycle 7  15600        15490204.
##  9 cycle 8  15600         3772204.
## 10 cycle 9  15600          755234.
## 11 cycle 10 15600          256605.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       197701352.
##  4 cycle 3  15600       197477623.
##  5 cycle 4  15600       152229308.
##  6 cycle 5  15600        92933946.
##  7 cycle 6  15600        48736774.
##  8 cycle 7  15600        14864907.
##  9 cycle 8  15600         3533938.
## 10 cycle 9  15600          811719.
## 11 cycle 10 15600          262643.
## 12 cycle 11 15600           58848.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252006927.
##  3 cycle 2  15600       197962796.
##  4 cycle 3  15600       198467075.
##  5 cycle 4  15600       152858607.
##  6 cycle 5  15600        93141254.
##  7 cycle 6  15600        48817472.
##  8 cycle 7  15600        14910517.
##  9 cycle 8  15600         3666296.
## 10 cycle 9  15600          828456.
## 11 cycle 10 15600          271699.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       197950564.
##  4 cycle 3  15600       196699429.
##  5 cycle 4  15600       151167471.
##  6 cycle 5  15600        92717719.
##  7 cycle 6  15600        48561471.
##  8 cycle 7  15600        15184678.
##  9 cycle 8  15600         3613175.
## 10 cycle 9  15600          784523.
## 11 cycle 10 15600          223397.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251973685.
##  3 cycle 2  15600       197240897.
##  4 cycle 3  15600       198619203.
##  5 cycle 4  15600       153327619.
##  6 cycle 5  15600        94160686.
##  7 cycle 6  15600        49809434.
##  8 cycle 7  15600        15178667.
##  9 cycle 8  15600         3616898.
## 10 cycle 9  15600          782431.
## 11 cycle 10 15600          232454.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252605281.
##  3 cycle 2  15600       197124445.
##  4 cycle 3  15600       198671799.
##  5 cycle 4  15600       153479974.
##  6 cycle 5  15600        94248407.
##  7 cycle 6  15600        50153081.
##  8 cycle 7  15600        15794395.
##  9 cycle 8  15600         3750799.
## 10 cycle 9  15600          740589.
## 11 cycle 10 15600          229435.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       197764675.
##  4 cycle 3  15600       198027770.
##  5 cycle 4  15600       152195592.
##  6 cycle 5  15600        93766610.
##  7 cycle 6  15600        49573046.
##  8 cycle 7  15600        14990081.
##  9 cycle 8  15600         3717539.
## 10 cycle 9  15600          845192.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       197457364.
##  4 cycle 3  15600       197008185.
##  5 cycle 4  15600       152748744.
##  6 cycle 5  15600        94118181.
##  7 cycle 6  15600        49635616.
##  8 cycle 7  15600        15256518.
##  9 cycle 8  15600         3592916.
## 10 cycle 9  15600          761510.
## 11 cycle 10 15600          229435.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252405830.
##  3 cycle 2  15600       200635834.
##  4 cycle 3  15600       199541579.
##  5 cycle 4  15600       154726628.
##  6 cycle 5  15600        95160559.
##  7 cycle 6  15600        50077842.
##  8 cycle 7  15600        15658329.
##  9 cycle 8  15600         3637602.
## 10 cycle 9  15600          813811.
## 11 cycle 10 15600          238492.
## 12 cycle 11 15600           61651.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       198331007.
##  4 cycle 3  15600       199322943.
##  5 cycle 4  15600       153796424.
##  6 cycle 5  15600        94407039.
##  7 cycle 6  15600        49774168.
##  8 cycle 7  15600        15120756.
##  9 cycle 8  15600         3641502.
## 10 cycle 9  15600          761510.
## 11 cycle 10 15600          226416.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       199332186.
##  4 cycle 3  15600       199663284.
##  5 cycle 4  15600       152677711.
##  6 cycle 5  15600        93532915.
##  7 cycle 6  15600        49053355.
##  8 cycle 7  15600        14988114.
##  9 cycle 8  15600         3549486.
## 10 cycle 9  15600          815904.
## 11 cycle 10 15600          283775.
## 12 cycle 11 15600           56046.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251292226.
##  3 cycle 2  15600       197777160.
##  4 cycle 3  15600       198391882.
##  5 cycle 4  15600       153796599.
##  6 cycle 5  15600        93619267.
##  7 cycle 6  15600        49603591.
##  8 cycle 7  15600        15041326.
##  9 cycle 8  15600         3636791.
## 10 cycle 9  15600          776154.
## 11 cycle 10 15600          241510.
## 12 cycle 11 15600           56046.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252306104.
##  3 cycle 2  15600       197478515.
##  4 cycle 3  15600       198446933.
##  5 cycle 4  15600       151104751.
##  6 cycle 5  15600        93274561.
##  7 cycle 6  15600        49215997.
##  8 cycle 7  15600        14539429.
##  9 cycle 8  15600         3481740.
## 10 cycle 9  15600          782431.
## 11 cycle 10 15600          223397.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       197734988.
##  4 cycle 3  15600       197519502.
##  5 cycle 4  15600       153317968.
##  6 cycle 5  15600        92404527.
##  7 cycle 6  15600        49020083.
##  8 cycle 7  15600        14945163.
##  9 cycle 8  15600         3504100.
## 10 cycle 9  15600          782431.
## 11 cycle 10 15600          232454.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       198191367.
##  4 cycle 3  15600       198602255.
##  5 cycle 4  15600       152874534.
##  6 cycle 5  15600        92614583.
##  7 cycle 6  15600        48255570.
##  8 cycle 7  15600        14670116.
##  9 cycle 8  15600         3735442.
## 10 cycle 9  15600          826364.
## 11 cycle 10 15600          280756.
## 12 cycle 11 15600           72860.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       198404267.
##  4 cycle 3  15600       196859819.
##  5 cycle 4  15600       151367957.
##  6 cycle 5  15600        93032271.
##  7 cycle 6  15600        48518267.
##  8 cycle 7  15600        14905697.
##  9 cycle 8  15600         3516146.
## 10 cycle 9  15600          794983.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           42035.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       196929382.
##  4 cycle 3  15600       196847214.
##  5 cycle 4  15600       151333109.
##  6 cycle 5  15600        93149470.
##  7 cycle 6  15600        48904376.
##  8 cycle 7  15600        14932436.
##  9 cycle 8  15600         3722806.
## 10 cycle 9  15600          834732.
## 11 cycle 10 15600          241510.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       195963113.
##  4 cycle 3  15600       197469506.
##  5 cycle 4  15600       152680880.
##  6 cycle 5  15600        93134407.
##  7 cycle 6  15600        49031762.
##  8 cycle 7  15600        15005855.
##  9 cycle 8  15600         3626367.
## 10 cycle 9  15600          797075.
## 11 cycle 10 15600          250567.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251724371.
##  3 cycle 2  15600       197374167.
##  4 cycle 3  15600       198196858.
##  5 cycle 4  15600       152404041.
##  6 cycle 5  15600        93024073.
##  7 cycle 6  15600        49090857.
##  8 cycle 7  15600        15715087.
##  9 cycle 8  15600         3879656.
## 10 cycle 9  15600          815904.
## 11 cycle 10 15600          232454.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251258984.
##  3 cycle 2  15600       198274055.
##  4 cycle 3  15600       196117407.
##  5 cycle 4  15600       152122902.
##  6 cycle 5  15600        93113830.
##  7 cycle 6  15600        49528105.
##  8 cycle 7  15600        14919382.
##  9 cycle 8  15600         3634468.
## 10 cycle 9  15600          740589.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       198870074.
##  4 cycle 3  15600       199341487.
##  5 cycle 4  15600       154014350.
##  6 cycle 5  15600        94896044.
##  7 cycle 6  15600        50107396.
##  8 cycle 7  15600        15169924.
##  9 cycle 8  15600         3512279.
## 10 cycle 9  15600          782431.
## 11 cycle 10 15600          238492.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       198217231.
##  4 cycle 3  15600       197575425.
##  5 cycle 4  15600       152942255.
##  6 cycle 5  15600        93663132.
##  7 cycle 6  15600        49103018.
##  8 cycle 7  15600        15084714.
##  9 cycle 8  15600         3551030.
## 10 cycle 9  15600          834732.
## 11 cycle 10 15600          262643.
## 12 cycle 11 15600           56046.
## 13 cycle 12 15600           20810.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251674508.
##  3 cycle 2  15600       198912628.
##  4 cycle 3  15600       196924875.
##  5 cycle 4  15600       152982741.
##  6 cycle 5  15600        94218254.
##  7 cycle 6  15600        49365723.
##  8 cycle 7  15600        15031380.
##  9 cycle 8  15600         3457392.
## 10 cycle 9  15600          792891.
## 11 cycle 10 15600          229435.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       197244464.
##  4 cycle 3  15600       197994443.
##  5 cycle 4  15600       152325220.
##  6 cycle 5  15600        93278327.
##  7 cycle 6  15600        49074710.
##  8 cycle 7  15600        15005406.
##  9 cycle 8  15600         3531360.
## 10 cycle 9  15600          765694.
## 11 cycle 10 15600          262643.
## 12 cycle 11 15600           58848.
## 13 cycle 12 15600           23411.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       200273866.
##  4 cycle 3  15600       200898178.
##  5 cycle 4  15600       154838322.
##  6 cycle 5  15600        94725717.
##  7 cycle 6  15600        50723425.
##  8 cycle 7  15600        15328795.
##  9 cycle 8  15600         3673552.
## 10 cycle 9  15600          807535.
## 11 cycle 10 15600          241510.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       196075742.
##  4 cycle 3  15600       197264926.
##  5 cycle 4  15600       151146400.
##  6 cycle 5  15600        92595393.
##  7 cycle 6  15600        48163702.
##  8 cycle 7  15600        14544116.
##  9 cycle 8  15600         3643936.
## 10 cycle 9  15600          740589.
## 11 cycle 10 15600          238492.
## 12 cycle 11 15600           42035.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251790855.
##  3 cycle 2  15600       196380377.
##  4 cycle 3  15600       196886916.
##  5 cycle 4  15600       149568789.
##  6 cycle 5  15600        92363752.
##  7 cycle 6  15600        48258302.
##  8 cycle 7  15600        14877257.
##  9 cycle 8  15600         3504100.
## 10 cycle 9  15600          694564.
## 11 cycle 10 15600          223397.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       198153018.
##  4 cycle 3  15600       198732949.
##  5 cycle 4  15600       152608828.
##  6 cycle 5  15600        94490662.
##  7 cycle 6  15600        49732705.
##  8 cycle 7  15600        15598717.
##  9 cycle 8  15600         3685822.
## 10 cycle 9  15600          817996.
## 11 cycle 10 15600          253586.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       199626627.
##  4 cycle 3  15600       199335983.
##  5 cycle 4  15600       152773767.
##  6 cycle 5  15600        91667097.
##  7 cycle 6  15600        47566283.
##  8 cycle 7  15600        14363132.
##  9 cycle 8  15600         3414328.
## 10 cycle 9  15600          732221.
## 11 cycle 10 15600          244529.
## 12 cycle 11 15600           72860.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       197926357.
##  4 cycle 3  15600       198528514.
##  5 cycle 4  15600       152590720.
##  6 cycle 5  15600        91840152.
##  7 cycle 6  15600        48536890.
##  8 cycle 7  15600        14717305.
##  9 cycle 8  15600         3507378.
## 10 cycle 9  15600          797075.
## 11 cycle 10 15600          244529.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       196415669.
##  4 cycle 3  15600       197311583.
##  5 cycle 4  15600       151889574.
##  6 cycle 5  15600        93422238.
##  7 cycle 6  15600        49291974.
##  8 cycle 7  15600        15218825.
##  9 cycle 8  15600         3602973.
## 10 cycle 9  15600          803351.
## 11 cycle 10 15600          244529.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       198357381.
##  4 cycle 3  15600       198183963.
##  5 cycle 4  15600       153805900.
##  6 cycle 5  15600        94551644.
##  7 cycle 6  15600        49512956.
##  8 cycle 7  15600        15150154.
##  9 cycle 8  15600         3667473.
## 10 cycle 9  15600          736405.
## 11 cycle 10 15600          241510.
## 12 cycle 11 15600           67255.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       200711133.
##  4 cycle 3  15600       200137076.
##  5 cycle 4  15600       153804069.
##  6 cycle 5  15600        92846567.
##  7 cycle 6  15600        48415719.
##  8 cycle 7  15600        14620765.
##  9 cycle 8  15600         3588937.
## 10 cycle 9  15600          824272.
## 11 cycle 10 15600          232454.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       197723012.
##  4 cycle 3  15600       198297406.
##  5 cycle 4  15600       153920095.
##  6 cycle 5  15600        94372102.
##  7 cycle 6  15600        48931446.
##  8 cycle 7  15600        15363258.
##  9 cycle 8  15600         3784617.
## 10 cycle 9  15600          780338.
## 11 cycle 10 15600          274718.
## 12 cycle 11 15600           67255.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       199548782.
##  4 cycle 3  15600       199677921.
##  5 cycle 4  15600       153811858.
##  6 cycle 5  15600        93059010.
##  7 cycle 6  15600        49422825.
##  8 cycle 7  15600        15100427.
##  9 cycle 8  15600         3757834.
## 10 cycle 9  15600          759418.
## 11 cycle 10 15600          202265.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       195662554.
##  4 cycle 3  15600       195605219.
##  5 cycle 4  15600       151197349.
##  6 cycle 5  15600        92632403.
##  7 cycle 6  15600        48649627.
##  8 cycle 7  15600        14725987.
##  9 cycle 8  15600         3485719.
## 10 cycle 9  15600          851469.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       198749418.
##  4 cycle 3  15600       198765695.
##  5 cycle 4  15600       154041728.
##  6 cycle 5  15600        93593221.
##  7 cycle 6  15600        49859830.
##  8 cycle 7  15600        15126463.
##  9 cycle 8  15600         3621577.
## 10 cycle 9  15600          807535.
## 11 cycle 10 15600          256605.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252389209.
##  3 cycle 2  15600       197779964.
##  4 cycle 3  15600       200340494.
##  5 cycle 4  15600       153933522.
##  6 cycle 5  15600        94059577.
##  7 cycle 6  15600        49341875.
##  8 cycle 7  15600        14399052.
##  9 cycle 8  15600         3395358.
## 10 cycle 9  15600          732221.
## 11 cycle 10 15600          235473.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600           20810.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600            2241.
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251225742.
##  3 cycle 2  15600       196798660.
##  4 cycle 3  15600       197925190.
##  5 cycle 4  15600       152786905.
##  6 cycle 5  15600        92876711.
##  7 cycle 6  15600        48544338.
##  8 cycle 7  15600        14642429.
##  9 cycle 8  15600         3491909.
## 10 cycle 9  15600          794983.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       199547634.
##  4 cycle 3  15600       199537381.
##  5 cycle 4  15600       155034477.
##  6 cycle 5  15600        94539986.
##  7 cycle 6  15600        49096060.
##  8 cycle 7  15600        14970057.
##  9 cycle 8  15600         3483396.
## 10 cycle 9  15600          740589.
## 11 cycle 10 15600          265662.
## 12 cycle 11 15600           64453.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       197293517.
##  4 cycle 3  15600       196987318.
##  5 cycle 4  15600       152781267.
##  6 cycle 5  15600        93574717.
##  7 cycle 6  15600        48439563.
##  8 cycle 7  15600        14477729.
##  9 cycle 8  15600         3650493.
## 10 cycle 9  15600          797075.
## 11 cycle 10 15600          226416.
## 12 cycle 11 15600           42035.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       197509602.
##  4 cycle 3  15600       199571566.
##  5 cycle 4  15600       154148103.
##  6 cycle 5  15600        93806701.
##  7 cycle 6  15600        49808192.
##  8 cycle 7  15600        15014343.
##  9 cycle 8  15600         3688066.
## 10 cycle 9  15600          817996.
## 11 cycle 10 15600          274718.
## 12 cycle 11 15600           58848.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       197227646.
##  4 cycle 3  15600       196852718.
##  5 cycle 4  15600       151722599.
##  6 cycle 5  15600        92628285.
##  7 cycle 6  15600        48699780.
##  8 cycle 7  15600        14421541.
##  9 cycle 8  15600         3576524.
## 10 cycle 9  15600          759418.
## 11 cycle 10 15600          208303.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       196534159.
##  4 cycle 3  15600       197481397.
##  5 cycle 4  15600       150970473.
##  6 cycle 5  15600        92956207.
##  7 cycle 6  15600        48725847.
##  8 cycle 7  15600        14901009.
##  9 cycle 8  15600         3688544.
## 10 cycle 9  15600          799167.
## 11 cycle 10 15600          202265.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       199085013.
##  4 cycle 3  15600       197125403.
##  5 cycle 4  15600       152252849.
##  6 cycle 5  15600        92522402.
##  7 cycle 6  15600        48577367.
##  8 cycle 7  15600        15008891.
##  9 cycle 8  15600         3602273.
## 10 cycle 9  15600          826364.
## 11 cycle 10 15600          253586.
## 12 cycle 11 15600           70058.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       196807069.
##  4 cycle 3  15600       197429236.
##  5 cycle 4  15600       151758290.
##  6 cycle 5  15600        94516355.
##  7 cycle 6  15600        49586701.
##  8 cycle 7  15600        15364011.
##  9 cycle 8  15600         3715582.
## 10 cycle 9  15600          740589.
## 11 cycle 10 15600          268680.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       197972733.
##  4 cycle 3  15600       198326245.
##  5 cycle 4  15600       151899369.
##  6 cycle 5  15600        92681070.
##  7 cycle 6  15600        49027288.
##  8 cycle 7  15600        15257404.
##  9 cycle 8  15600         3703869.
## 10 cycle 9  15600          769878.
## 11 cycle 10 15600          235473.
## 12 cycle 11 15600           56046.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            7244.
## 15 cycle 14 15600            2241.
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       198106003.
##  4 cycle 3  15600       198850747.
##  5 cycle 4  15600       152076428.
##  6 cycle 5  15600        92546717.
##  7 cycle 6  15600        48878795.
##  8 cycle 7  15600        14996031.
##  9 cycle 8  15600         3520125.
## 10 cycle 9  15600          759418.
## 11 cycle 10 15600          208303.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       196049496.
##  4 cycle 3  15600       196710291.
##  5 cycle 4  15600       151475495.
##  6 cycle 5  15600        91950829.
##  7 cycle 6  15600        48622304.
##  8 cycle 7  15600        14860596.
##  9 cycle 8  15600         3464505.
## 10 cycle 9  15600          794983.
## 11 cycle 10 15600          259624.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            7244.
## 15 cycle 14 15600            2241.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252090032.
##  3 cycle 2  15600       198187800.
##  4 cycle 3  15600       198732368.
##  5 cycle 4  15600       153332413.
##  6 cycle 5  15600        94489625.
##  7 cycle 6  15600        49325989.
##  8 cycle 7  15600        15564315.
##  9 cycle 8  15600         3686411.
## 10 cycle 9  15600          792891.
## 11 cycle 10 15600          265662.
## 12 cycle 11 15600           67255.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       198097085.
##  4 cycle 3  15600       198300164.
##  5 cycle 4  15600       153290096.
##  6 cycle 5  15600        93838223.
##  7 cycle 6  15600        49639590.
##  8 cycle 7  15600        14813468.
##  9 cycle 8  15600         3728329.
## 10 cycle 9  15600          751050.
## 11 cycle 10 15600          232454.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       198543398.
##  4 cycle 3  15600       199429575.
##  5 cycle 4  15600       153874115.
##  6 cycle 5  15600        94409796.
##  7 cycle 6  15600        49595648.
##  8 cycle 7  15600        15293069.
##  9 cycle 8  15600         3457948.
## 10 cycle 9  15600          784523.
## 11 cycle 10 15600          307926.
## 12 cycle 11 15600           81267.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251807476.
##  3 cycle 2  15600       197305111.
##  4 cycle 3  15600       196153188.
##  5 cycle 4  15600       152440227.
##  6 cycle 5  15600        93110758.
##  7 cycle 6  15600        48806054.
##  8 cycle 7  15600        15214138.
##  9 cycle 8  15600         3533572.
## 10 cycle 9  15600          755234.
## 11 cycle 10 15600          277737.
## 12 cycle 11 15600           86872.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       198533842.
##  4 cycle 3  15600       197086572.
##  5 cycle 4  15600       150982099.
##  6 cycle 5  15600        93171056.
##  7 cycle 6  15600        50022472.
##  8 cycle 7  15600        15396446.
##  9 cycle 8  15600         3764247.
## 10 cycle 9  15600          790799.
## 11 cycle 10 15600          217359.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600            4483.
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252422451.
##  3 cycle 2  15600       197157062.
##  4 cycle 3  15600       197851145.
##  5 cycle 4  15600       152884504.
##  6 cycle 5  15600        94382391.
##  7 cycle 6  15600        50205970.
##  8 cycle 7  15600        15182650.
##  9 cycle 8  15600         3696946.
## 10 cycle 9  15600          788707.
## 11 cycle 10 15600          214341.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       198139257.
##  4 cycle 3  15600       197756827.
##  5 cycle 4  15600       151724924.
##  6 cycle 5  15600        94692131.
##  7 cycle 6  15600        49887643.
##  8 cycle 7  15600        14959286.
##  9 cycle 8  15600         3597038.
## 10 cycle 9  15600          732221.
## 11 cycle 10 15600          241510.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252173137.
##  3 cycle 2  15600       198747890.
##  4 cycle 3  15600       198468817.
##  5 cycle 4  15600       153745475.
##  6 cycle 5  15600        95009107.
##  7 cycle 6  15600        50168967.
##  8 cycle 7  15600        14977537.
##  9 cycle 8  15600         3588937.
## 10 cycle 9  15600          834732.
## 11 cycle 10 15600          268680.
## 12 cycle 11 15600           61651.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       197415192.
##  4 cycle 3  15600       197513272.
##  5 cycle 4  15600       150440892.
##  6 cycle 5  15600        92711880.
##  7 cycle 6  15600        48259782.
##  8 cycle 7  15600        14618992.
##  9 cycle 8  15600         3694845.
## 10 cycle 9  15600          765694.
## 11 cycle 10 15600          214341.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       197951583.
##  4 cycle 3  15600       196782739.
##  5 cycle 4  15600       150251538.
##  6 cycle 5  15600        90992397.
##  7 cycle 6  15600        47966046.
##  8 cycle 7  15600        14865915.
##  9 cycle 8  15600         3707036.
## 10 cycle 9  15600          782431.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           58848.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       197516483.
##  4 cycle 3  15600       198841904.
##  5 cycle 4  15600       153784335.
##  6 cycle 5  15600        93515104.
##  7 cycle 6  15600        49643569.
##  8 cycle 7  15600        15225601.
##  9 cycle 8  15600         3868532.
## 10 cycle 9  15600          828456.
## 11 cycle 10 15600          220378.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       196959450.
##  4 cycle 3  15600       198602546.
##  5 cycle 4  15600       152396572.
##  6 cycle 5  15600        92894188.
##  7 cycle 6  15600        48914061.
##  8 cycle 7  15600        14582186.
##  9 cycle 8  15600         3569522.
## 10 cycle 9  15600          803351.
## 11 cycle 10 15600          232454.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       198137091.
##  4 cycle 3  15600       198441865.
##  5 cycle 4  15600       152476062.
##  6 cycle 5  15600        92217777.
##  7 cycle 6  15600        48785937.
##  8 cycle 7  15600        14982856.
##  9 cycle 8  15600         3631444.
## 10 cycle 9  15600          702932.
## 11 cycle 10 15600          220378.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       198179773.
##  4 cycle 3  15600       198147455.
##  5 cycle 4  15600       151592158.
##  6 cycle 5  15600        92405878.
##  7 cycle 6  15600        48270710.
##  8 cycle 7  15600        14492422.
##  9 cycle 8  15600         3413150.
## 10 cycle 9  15600          817996.
## 11 cycle 10 15600          223397.
## 12 cycle 11 15600           72860.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            7244.
## 15 cycle 14 15600            4483.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       196309793.
##  4 cycle 3  15600       197032524.
##  5 cycle 4  15600       154716195.
##  6 cycle 5  15600        95082784.
##  7 cycle 6  15600        50502438.
##  8 cycle 7  15600        15498377.
##  9 cycle 8  15600         3819023.
## 10 cycle 9  15600          788707.
## 11 cycle 10 15600          262643.
## 12 cycle 11 15600           72860.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       197472144.
##  4 cycle 3  15600       197626570.
##  5 cycle 4  15600       151673162.
##  6 cycle 5  15600        93789232.
##  7 cycle 6  15600        48992032.
##  8 cycle 7  15600        15327022.
##  9 cycle 8  15600         3609164.
## 10 cycle 9  15600          815904.
## 11 cycle 10 15600          268680.
## 12 cycle 11 15600           61651.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            9659.
## 15 cycle 14 15600               0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       197404745.
##  4 cycle 3  15600       198294079.
##  5 cycle 4  15600       153870802.
##  6 cycle 5  15600        94599284.
##  7 cycle 6  15600        49861077.
##  8 cycle 7  15600        14987992.
##  9 cycle 8  15600         3589382.
## 10 cycle 9  15600          830548.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       198062811.
##  4 cycle 3  15600       197324914.
##  5 cycle 4  15600       153365780.
##  6 cycle 5  15600        94162046.
##  7 cycle 6  15600        49797512.
##  8 cycle 7  15600        14972218.
##  9 cycle 8  15600         3698012.
## 10 cycle 9  15600          736405.
## 11 cycle 10 15600          256605.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       196420764.
##  4 cycle 3  15600       195613772.
##  5 cycle 4  15600       150892988.
##  6 cycle 5  15600        92346616.
##  7 cycle 6  15600        48351660.
##  8 cycle 7  15600        14838993.
##  9 cycle 8  15600         3253389.
## 10 cycle 9  15600          755234.
## 11 cycle 10 15600          223397.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251873959.
##  3 cycle 2  15600       199513489.
##  4 cycle 3  15600       199377704.
##  5 cycle 4  15600       153073539.
##  6 cycle 5  15600        94481076.
##  7 cycle 6  15600        49660455.
##  8 cycle 7  15600        15517065.
##  9 cycle 8  15600         3724095.
## 10 cycle 9  15600          771970.
## 11 cycle 10 15600          235473.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       197224461.
##  4 cycle 3  15600       196082193.
##  5 cycle 4  15600       149947352.
##  6 cycle 5  15600        93464040.
##  7 cycle 6  15600        48990038.
##  8 cycle 7  15600        14626278.
##  9 cycle 8  15600         3490365.
## 10 cycle 9  15600          765694.
## 11 cycle 10 15600          277737.
## 12 cycle 11 15600           64453.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       198690809.
##  4 cycle 3  15600       199712699.
##  5 cycle 4  15600       154316530.
##  6 cycle 5  15600        94342263.
##  7 cycle 6  15600        48981590.
##  8 cycle 7  15600        15008381.
##  9 cycle 8  15600         3657416.
## 10 cycle 9  15600          780338.
## 11 cycle 10 15600          250567.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       197318871.
##  4 cycle 3  15600       197950980.
##  5 cycle 4  15600       153057931.
##  6 cycle 5  15600        92500483.
##  7 cycle 6  15600        49809186.
##  8 cycle 7  15600        14828926.
##  9 cycle 8  15600         3479973.
## 10 cycle 9  15600          707116.
## 11 cycle 10 15600          262643.
## 12 cycle 11 15600           64453.
## 13 cycle 12 15600           20810.
## 14 cycle 13 15600            7244.
## 15 cycle 14 15600               0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       197178212.
##  4 cycle 3  15600       197594695.
##  5 cycle 4  15600       152473418.
##  6 cycle 5  15600        93184434.
##  7 cycle 6  15600        50019988.
##  8 cycle 7  15600        15333482.
##  9 cycle 8  15600         3577669.
## 10 cycle 9  15600          784523.
## 11 cycle 10 15600          271699.
## 12 cycle 11 15600           58848.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            7244.
## 15 cycle 14 15600               0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252056790.
##  3 cycle 2  15600       198061792.
##  4 cycle 3  15600       198248003.
##  5 cycle 4  15600       152303798.
##  6 cycle 5  15600        93435256.
##  7 cycle 6  15600        49894844.
##  8 cycle 7  15600        14812266.
##  9 cycle 8  15600         3607875.
## 10 cycle 9  15600          813811.
## 11 cycle 10 15600          262643.
## 12 cycle 11 15600           70058.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       196430830.
##  4 cycle 3  15600       196912706.
##  5 cycle 4  15600       150556249.
##  6 cycle 5  15600        93289291.
##  7 cycle 6  15600        49303896.
##  8 cycle 7  15600        15398036.
##  9 cycle 8  15600         3646482.
## 10 cycle 9  15600          872389.
## 11 cycle 10 15600          310945.
## 12 cycle 11 15600           70058.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       199119795.
##  4 cycle 3  15600       198838432.
##  5 cycle 4  15600       151556148.
##  6 cycle 5  15600        92976766.
##  7 cycle 6  15600        49164840.
##  8 cycle 7  15600        14998375.
##  9 cycle 8  15600         3507457.
## 10 cycle 9  15600          742681.
## 11 cycle 10 15600          214341.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252023548.
##  3 cycle 2  15600       197140754.
##  4 cycle 3  15600       198633549.
##  5 cycle 4  15600       152846837.
##  6 cycle 5  15600        93602492.
##  7 cycle 6  15600        49894106.
##  8 cycle 7  15600        15107337.
##  9 cycle 8  15600         3732052.
## 10 cycle 9  15600          838916.
## 11 cycle 10 15600          262643.
## 12 cycle 11 15600           61651.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       197590634.
##  4 cycle 3  15600       197688445.
##  5 cycle 4  15600       152378639.
##  6 cycle 5  15600        94144226.
##  7 cycle 6  15600        49720045.
##  8 cycle 7  15600        14866486.
##  9 cycle 8  15600         3427775.
## 10 cycle 9  15600          769878.
## 11 cycle 10 15600          274718.
## 12 cycle 11 15600           64453.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       250527662.
##  3 cycle 2  15600       193840480.
##  4 cycle 3  15600       196142326.
##  5 cycle 4  15600       150597579.
##  6 cycle 5  15600        92616979.
##  7 cycle 6  15600        49191911.
##  8 cycle 7  15600        14898860.
##  9 cycle 8  15600         3662206.
## 10 cycle 9  15600          794983.
## 11 cycle 10 15600          277737.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       196600029.
##  4 cycle 3  15600       197208713.
##  5 cycle 4  15600       151211126.
##  6 cycle 5  15600        92610123.
##  7 cycle 6  15600        48903876.
##  8 cycle 7  15600        14652496.
##  9 cycle 8  15600         3380033.
## 10 cycle 9  15600          738497.
## 11 cycle 10 15600          241510.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       198926517.
##  4 cycle 3  15600       199042867.
##  5 cycle 4  15600       154501438.
##  6 cycle 5  15600        94190479.
##  7 cycle 6  15600        49393779.
##  8 cycle 7  15600        14730929.
##  9 cycle 8  15600         3582459.
## 10 cycle 9  15600          817996.
## 11 cycle 10 15600          229435.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251308847.
##  3 cycle 2  15600       197728107.
##  4 cycle 3  15600       198400289.
##  5 cycle 4  15600       151922735.
##  6 cycle 5  15600        92064253.
##  7 cycle 6  15600        48679168.
##  8 cycle 7  15600        14859965.
##  9 cycle 8  15600         3529116.
## 10 cycle 9  15600          780338.
## 11 cycle 10 15600          193208.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252239620.
##  3 cycle 2  15600       198739990.
##  4 cycle 3  15600       198493445.
##  5 cycle 4  15600       153386007.
##  6 cycle 5  15600        93457517.
##  7 cycle 6  15600        49648029.
##  8 cycle 7  15600        14737827.
##  9 cycle 8  15600         3534050.
## 10 cycle 9  15600          755234.
## 11 cycle 10 15600          214341.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       197647586.
##  4 cycle 3  15600       198370724.
##  5 cycle 4  15600       153088128.
##  6 cycle 5  15600        93042920.
##  7 cycle 6  15600        49066519.
##  8 cycle 7  15600        14985648.
##  9 cycle 8  15600         3713671.
## 10 cycle 9  15600          786615.
## 11 cycle 10 15600          289813.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       198610287.
##  4 cycle 3  15600       198029802.
##  5 cycle 4  15600       152598714.
##  6 cycle 5  15600        93984892.
##  7 cycle 6  15600        48951802.
##  8 cycle 7  15600        14867882.
##  9 cycle 8  15600         3612108.
## 10 cycle 9  15600          738497.
## 11 cycle 10 15600          253586.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       198325146.
##  4 cycle 3  15600       197656135.
##  5 cycle 4  15600       152917665.
##  6 cycle 5  15600        93304003.
##  7 cycle 6  15600        49442690.
##  8 cycle 7  15600        15258728.
##  9 cycle 8  15600         3560276.
## 10 cycle 9  15600          786615.
## 11 cycle 10 15600          277737.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       198889058.
##  4 cycle 3  15600       197962278.
##  5 cycle 4  15600       152324057.
##  6 cycle 5  15600        92619718.
##  7 cycle 6  15600        48436579.
##  8 cycle 7  15600        14837414.
##  9 cycle 8  15600         3636568.
## 10 cycle 9  15600          738497.
## 11 cycle 10 15600          244529.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251923822.
##  3 cycle 2  15600       197449593.
##  4 cycle 3  15600       198342611.
##  5 cycle 4  15600       152957863.
##  6 cycle 5  15600        93842341.
##  7 cycle 6  15600        49033242.
##  8 cycle 7  15600        15146985.
##  9 cycle 8  15600         3395836.
## 10 cycle 9  15600          707116.
## 11 cycle 10 15600          247548.
## 12 cycle 11 15600           75662.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            9659.
## 15 cycle 14 15600            2241.
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       196125942.
##  4 cycle 3  15600       195886298.
##  5 cycle 4  15600       152658646.
##  6 cycle 5  15600        94396436.
##  7 cycle 6  15600        50433910.
##  8 cycle 7  15600        15580029.
##  9 cycle 8  15600         3784028.
## 10 cycle 9  15600          780338.
## 11 cycle 10 15600          229435.
## 12 cycle 11 15600           42035.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       195458191.
##  4 cycle 3  15600       196592493.
##  5 cycle 4  15600       152325713.
##  6 cycle 5  15600        93546627.
##  7 cycle 6  15600        48947832.
##  8 cycle 7  15600        14736309.
##  9 cycle 8  15600         3462548.
## 10 cycle 9  15600          748958.
## 11 cycle 10 15600          220378.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       198106003.
##  4 cycle 3  15600       197362728.
##  5 cycle 4  15600       153340727.
##  6 cycle 5  15600        94847719.
##  7 cycle 6  15600        49933084.
##  8 cycle 7  15600        15425335.
##  9 cycle 8  15600         3699779.
## 10 cycle 9  15600          780338.
## 11 cycle 10 15600          223397.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       197720336.
##  4 cycle 3  15600       197167269.
##  5 cycle 4  15600       154378643.
##  6 cycle 5  15600        94792214.
##  7 cycle 6  15600        49872998.
##  8 cycle 7  15600        15696969.
##  9 cycle 8  15600         3887469.
## 10 cycle 9  15600          861929.
## 11 cycle 10 15600          292831.
## 12 cycle 11 15600           67255.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       196571618.
##  4 cycle 3  15600       197796239.
##  5 cycle 4  15600       152024809.
##  6 cycle 5  15600        93561005.
##  7 cycle 6  15600        49289733.
##  8 cycle 7  15600        14983171.
##  9 cycle 8  15600         3501044.
## 10 cycle 9  15600          807535.
## 11 cycle 10 15600          241510.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       198612835.
##  4 cycle 3  15600       198049798.
##  5 cycle 4  15600       151737219.
##  6 cycle 5  15600        93826926.
##  7 cycle 6  15600        49121645.
##  8 cycle 7  15600        14832096.
##  9 cycle 8  15600         3416062.
## 10 cycle 9  15600          794983.
## 11 cycle 10 15600          214341.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251358710.
##  3 cycle 2  15600       197623378.
##  4 cycle 3  15600       197220882.
##  5 cycle 4  15600       151969878.
##  6 cycle 5  15600        93162830.
##  7 cycle 6  15600        49702165.
##  8 cycle 7  15600        15343428.
##  9 cycle 8  15600         3708547.
## 10 cycle 9  15600          786615.
## 11 cycle 10 15600          238492.
## 12 cycle 11 15600           64453.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       198452173.
##  4 cycle 3  15600       199387563.
##  5 cycle 4  15600       152138160.
##  6 cycle 5  15600        92749232.
##  7 cycle 6  15600        49076694.
##  8 cycle 7  15600        15127349.
##  9 cycle 8  15600         3517992.
## 10 cycle 9  15600          803351.
## 11 cycle 10 15600          256605.
## 12 cycle 11 15600           58848.
## 13 cycle 12 15600           18209.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       198907533.
##  4 cycle 3  15600       197033540.
##  5 cycle 4  15600       151873298.
##  6 cycle 5  15600        92900017.
##  7 cycle 6  15600        49248527.
##  8 cycle 7  15600        15553046.
##  9 cycle 8  15600         3857041.
## 10 cycle 9  15600          853561.
## 11 cycle 10 15600          235473.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       196296032.
##  4 cycle 3  15600       198298277.
##  5 cycle 4  15600       153009625.
##  6 cycle 5  15600        92430914.
##  7 cycle 6  15600        49028031.
##  8 cycle 7  15600        14819042.
##  9 cycle 8  15600         3533683.
## 10 cycle 9  15600          755234.
## 11 cycle 10 15600          220378.
## 12 cycle 11 15600           42035.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            7244.
## 15 cycle 14 15600               0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252206378.
##  3 cycle 2  15600       197946360.
##  4 cycle 3  15600       199377849.
##  5 cycle 4  15600       154712532.
##  6 cycle 5  15600        94327552.
##  7 cycle 6  15600        48955033.
##  8 cycle 7  15600        14862248.
##  9 cycle 8  15600         3685710.
## 10 cycle 9  15600          790799.
## 11 cycle 10 15600          265662.
## 12 cycle 11 15600           70058.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0
# Females
discounted_costs_f_alt <-
  map(final_cost_f2_alt, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_f_alt
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       140231107.
##  4 cycle 3  10400       117483919.
##  5 cycle 4  10400       121129189.
##  6 cycle 5  10400        86677207.
##  7 cycle 6  10400        63883740.
##  8 cycle 7  10400        26034598.
##  9 cycle 8  10400         5227367.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       138486365.
##  4 cycle 3  10400       116751445.
##  5 cycle 4  10400       119257472.
##  6 cycle 5  10400        85588274.
##  7 cycle 6  10400        62734343.
##  8 cycle 7  10400        26326849.
##  9 cycle 8  10400         5636632.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       140266678.
##  4 cycle 3  10400       117326141.
##  5 cycle 4  10400       120013633.
##  6 cycle 5  10400        86352436.
##  7 cycle 6  10400        62613557.
##  8 cycle 7  10400        25792780.
##  9 cycle 8  10400         5316228.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       140099255.
##  4 cycle 3  10400       117579459.
##  5 cycle 4  10400       119938745.
##  6 cycle 5  10400        85979306.
##  7 cycle 6  10400        63493553.
##  8 cycle 7  10400        25945953.
##  9 cycle 8  10400         5090812.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           55545.
## 12 cycle 11 10400               0 
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       139535485.
##  4 cycle 3  10400       117465720.
##  5 cycle 4  10400       119186545.
##  6 cycle 5  10400        85852842.
##  7 cycle 6  10400        61863901.
##  8 cycle 7  10400        25667799.
##  9 cycle 8  10400         5425890.
## 10 cycle 9  10400          357976.
## 11 cycle 10 10400          183298.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       140214823.
##  4 cycle 3  10400       116794393.
##  5 cycle 4  10400       120825554.
##  6 cycle 5  10400        86736262.
##  7 cycle 6  10400        63581347.
##  8 cycle 7  10400        26304922.
##  9 cycle 8  10400         5306770.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       139905903.
##  4 cycle 3  10400       117404393.
##  5 cycle 4  10400       119923368.
##  6 cycle 5  10400        86220390.
##  7 cycle 6  10400        62723008.
##  8 cycle 7  10400        25937182.
##  9 cycle 8  10400         5563422.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220636028.
##  3 cycle 2  10400       141166873.
##  4 cycle 3  10400       117741967.
##  5 cycle 4  10400       120110849.
##  6 cycle 5  10400        86347087.
##  7 cycle 6  10400        63653228.
##  8 cycle 7  10400        26000457.
##  9 cycle 8  10400         5493950.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       139659590.
##  4 cycle 3  10400       115906143.
##  5 cycle 4  10400       119714168.
##  6 cycle 5  10400        86403585.
##  7 cycle 6  10400        62654690.
##  8 cycle 7  10400        25664354.
##  9 cycle 8  10400         5305793.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221411398.
##  3 cycle 2  10400       140709345.
##  4 cycle 3  10400       117218955.
##  5 cycle 4  10400       121157459.
##  6 cycle 5  10400        87272351.
##  7 cycle 6  10400        64329037.
##  8 cycle 7  10400        26905396.
##  9 cycle 8  10400         5470758.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       140402640.
##  4 cycle 3  10400       117444064.
##  5 cycle 4  10400       120483621.
##  6 cycle 5  10400        85832382.
##  7 cycle 6  10400        62531754.
##  8 cycle 7  10400        25695990.
##  9 cycle 8  10400         5140829.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       139658483.
##  4 cycle 3  10400       117046254.
##  5 cycle 4  10400       120077124.
##  6 cycle 5  10400        85791696.
##  7 cycle 6  10400        63146095.
##  8 cycle 7  10400        26461225.
##  9 cycle 8  10400         5526666.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       140094828.
##  4 cycle 3  10400       116346719.
##  5 cycle 4  10400       119453779.
##  6 cycle 5  10400        85321859.
##  7 cycle 6  10400        62387161.
##  8 cycle 7  10400        25558479.
##  9 cycle 8  10400         5352748.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220463723.
##  3 cycle 2  10400       139431775.
##  4 cycle 3  10400       117394383.
##  5 cycle 4  10400       120517347.
##  6 cycle 5  10400        85900734.
##  7 cycle 6  10400        63623892.
##  8 cycle 7  10400        25930604.
##  9 cycle 8  10400         5781231.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       140596780.
##  4 cycle 3  10400       117530323.
##  5 cycle 4  10400       120523793.
##  6 cycle 5  10400        85557594.
##  7 cycle 6  10400        62671278.
##  8 cycle 7  10400        25806875.
##  9 cycle 8  10400         5457867.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       140304145.
##  4 cycle 3  10400       118425306.
##  5 cycle 4  10400       120442249.
##  6 cycle 5  10400        86827630.
##  7 cycle 6  10400        63202402.
##  8 cycle 7  10400        26995295.
##  9 cycle 8  10400         5898938.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       139732946.
##  4 cycle 3  10400       116393851.
##  5 cycle 4  10400       119468062.
##  6 cycle 5  10400        85876783.
##  7 cycle 6  10400        63876430.
##  8 cycle 7  10400        26426459.
##  9 cycle 8  10400         6037010.
## 10 cycle 9  10400          381071.
## 11 cycle 10 10400          172189.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400               0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       139130920.
##  4 cycle 3  10400       117376550.
##  5 cycle 4  10400       121083161.
##  6 cycle 5  10400        86249446.
##  7 cycle 6  10400        62267973.
##  8 cycle 7  10400        25770225.
##  9 cycle 8  10400         5335683.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220722180.
##  3 cycle 2  10400       141723684.
##  4 cycle 3  10400       117487375.
##  5 cycle 4  10400       119769907.
##  6 cycle 5  10400        85229566.
##  7 cycle 6  10400        62747644.
##  8 cycle 7  10400        26104137.
##  9 cycle 8  10400         5306163.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       139326168.
##  4 cycle 3  10400       117624227.
##  5 cycle 4  10400       119811974.
##  6 cycle 5  10400        86020692.
##  7 cycle 6  10400        63383826.
##  8 cycle 7  10400        25857305.
##  9 cycle 8  10400         5251972.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220743718.
##  3 cycle 2  10400       139312886.
##  4 cycle 3  10400       117226233.
##  5 cycle 4  10400       120359926.
##  6 cycle 5  10400        86175522.
##  7 cycle 6  10400        64197193.
##  8 cycle 7  10400        26773836.
##  9 cycle 8  10400         5768340.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       140073327.
##  4 cycle 3  10400       117539604.
##  5 cycle 4  10400       120816917.
##  6 cycle 5  10400        86192724.
##  7 cycle 6  10400        63345213.
##  8 cycle 7  10400        26527946.
##  9 cycle 8  10400         5504384.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       139079854.
##  4 cycle 3  10400       117259718.
##  5 cycle 4  10400       119142602.
##  6 cycle 5  10400        84403792.
##  7 cycle 6  10400        61627675.
##  8 cycle 7  10400        25654016.
##  9 cycle 8  10400         5262643.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       139929618.
##  4 cycle 3  10400       116261005.
##  5 cycle 4  10400       119948562.
##  6 cycle 5  10400        85511326.
##  7 cycle 6  10400        62819894.
##  8 cycle 7  10400        26103823.
##  9 cycle 8  10400         5317271.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       138548653.
##  4 cycle 3  10400       116703583.
##  5 cycle 4  10400       119377102.
##  6 cycle 5  10400        85140288.
##  7 cycle 6  10400        63014711.
##  8 cycle 7  10400        26183072.
##  9 cycle 8  10400         5220063.
## 10 cycle 9  10400          234802.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       138897571.
##  4 cycle 3  10400       117179829.
##  5 cycle 4  10400       120563480.
##  6 cycle 5  10400        86157153.
##  7 cycle 6  10400        63525131.
##  8 cycle 7  10400        26297091.
##  9 cycle 8  10400         5699911.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       139013139.
##  4 cycle 3  10400       116866820.
##  5 cycle 4  10400       119430860.
##  6 cycle 5  10400        84901304.
##  7 cycle 6  10400        63096976.
##  8 cycle 7  10400        25814081.
##  9 cycle 8  10400         5673152.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       140516784.
##  4 cycle 3  10400       118700280.
##  5 cycle 4  10400       121223414.
##  6 cycle 5  10400        86005347.
##  7 cycle 6  10400        63202033.
##  8 cycle 7  10400        26438360.
##  9 cycle 8  10400         5337770.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       139694372.
##  4 cycle 3  10400       117418041.
##  5 cycle 4  10400       120198039.
##  6 cycle 5  10400        85482736.
##  7 cycle 6  10400        62967006.
##  8 cycle 7  10400        25711966.
##  9 cycle 8  10400         5468604.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       140631562.
##  4 cycle 3  10400       116587117.
##  5 cycle 4  10400       120022965.
##  6 cycle 5  10400        86061836.
##  7 cycle 6  10400        63428798.
##  8 cycle 7  10400        25804995.
##  9 cycle 8  10400         5585031.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220528337.
##  3 cycle 2  10400       139195422.
##  4 cycle 3  10400       116650809.
##  5 cycle 4  10400       118700589.
##  6 cycle 5  10400        84347770.
##  7 cycle 6  10400        62424362.
##  8 cycle 7  10400        25525275.
##  9 cycle 8  10400         5207912.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       140253714.
##  4 cycle 3  10400       117235514.
##  5 cycle 4  10400       118688982.
##  6 cycle 5  10400        84746232.
##  7 cycle 6  10400        62574299.
##  8 cycle 7  10400        26122931.
##  9 cycle 8  10400         5427741.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       140095934.
##  4 cycle 3  10400       116956356.
##  5 cycle 4  10400       120603356.
##  6 cycle 5  10400        86463788.
##  7 cycle 6  10400        63465629.
##  8 cycle 7  10400        26401713.
##  9 cycle 8  10400         5888370.
## 10 cycle 9  10400          327183.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220851408.
##  3 cycle 2  10400       139824011.
##  4 cycle 3  10400       117022051.
##  5 cycle 4  10400       120239706.
##  6 cycle 5  10400        86344997.
##  7 cycle 6  10400        64032080.
##  8 cycle 7  10400        26098184.
##  9 cycle 8  10400         5269407.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       140447855.
##  4 cycle 3  10400       119063877.
##  5 cycle 4  10400       119989028.
##  6 cycle 5  10400        85828909.
##  7 cycle 6  10400        62044927.
##  8 cycle 7  10400        25675318.
##  9 cycle 8  10400         5262946.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       139075427.
##  4 cycle 3  10400       117267724.
##  5 cycle 4  10400       120114220.
##  6 cycle 5  10400        84504224.
##  7 cycle 6  10400        61748645.
##  8 cycle 7  10400        25365526.
##  9 cycle 8  10400         5188997.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400           72208.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       140150003.
##  4 cycle 3  10400       118136684.
##  5 cycle 4  10400       120527964.
##  6 cycle 5  10400        86489596.
##  7 cycle 6  10400        63844297.
##  8 cycle 7  10400        26550500.
##  9 cycle 8  10400         5588464.
## 10 cycle 9  10400          377222.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       140604210.
##  4 cycle 3  10400       118160159.
##  5 cycle 4  10400       119765841.
##  6 cycle 5  10400        85981639.
##  7 cycle 6  10400        62547390.
##  8 cycle 7  10400        25437884.
##  9 cycle 8  10400         5233627.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          172189.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       140749816.
##  4 cycle 3  10400       117528321.
##  5 cycle 4  10400       118914254.
##  6 cycle 5  10400        84459823.
##  7 cycle 6  10400        62186907.
##  8 cycle 7  10400        25831622.
##  9 cycle 8  10400         5180716.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       141385045.
##  4 cycle 3  10400       117759984.
##  5 cycle 4  10400       120609107.
##  6 cycle 5  10400        86794150.
##  7 cycle 6  10400        63577507.
##  8 cycle 7  10400        26353160.
##  9 cycle 8  10400         5242041.
## 10 cycle 9  10400          215556.
## 11 cycle 10 10400           55545.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       140851630.
##  4 cycle 3  10400       117374002.
##  5 cycle 4  10400       120368752.
##  6 cycle 5  10400        85702427.
##  7 cycle 6  10400        63363951.
##  8 cycle 7  10400        25223003.
##  9 cycle 8  10400         5206062.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       140254821.
##  4 cycle 3  10400       116446809.
##  5 cycle 4  10400       120203305.
##  6 cycle 5  10400        86518195.
##  7 cycle 6  10400        64140118.
##  8 cycle 7  10400        26563969.
##  9 cycle 8  10400         5668305.
## 10 cycle 9  10400          361825.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220399109.
##  3 cycle 2  10400       137771457.
##  4 cycle 3  10400       115991311.
##  5 cycle 4  10400       119746208.
##  6 cycle 5  10400        85693122.
##  7 cycle 6  10400        62923539.
##  8 cycle 7  10400        25894268.
##  9 cycle 8  10400         5447432.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221454474.
##  3 cycle 2  10400       140222570.
##  4 cycle 3  10400       117194569.
##  5 cycle 4  10400       119562097.
##  6 cycle 5  10400        85223517.
##  7 cycle 6  10400        62205277.
##  8 cycle 7  10400        25831310.
##  9 cycle 8  10400         5158804.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          183298.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       139657377.
##  4 cycle 3  10400       117495020.
##  5 cycle 4  10400       119811574.
##  6 cycle 5  10400        85299075.
##  7 cycle 6  10400        62563332.
##  8 cycle 7  10400        25826923.
##  9 cycle 8  10400         5624918.
## 10 cycle 9  10400          408016.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       140236323.
##  4 cycle 3  10400       117138155.
##  5 cycle 4  10400       119194086.
##  6 cycle 5  10400        85324416.
##  7 cycle 6  10400        62686084.
##  8 cycle 7  10400        25601705.
##  9 cycle 8  10400         5348877.
## 10 cycle 9  10400          342579.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       138848718.
##  4 cycle 3  10400       116540346.
##  5 cycle 4  10400       119707027.
##  6 cycle 5  10400        85172368.
##  7 cycle 6  10400        62635767.
##  8 cycle 7  10400        25958482.
##  9 cycle 8  10400         5163045.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          172189.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       140222253.
##  4 cycle 3  10400       117190929.
##  5 cycle 4  10400       120391271.
##  6 cycle 5  10400        86597934.
##  7 cycle 6  10400        63318887.
##  8 cycle 7  10400        25865450.
##  9 cycle 8  10400         5311077.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       140747920.
##  4 cycle 3  10400       118203107.
##  5 cycle 4  10400       120787257.
##  6 cycle 5  10400        87419984.
##  7 cycle 6  10400        63820583.
##  8 cycle 7  10400        26465925.
##  9 cycle 8  10400         5317945.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       139301347.
##  4 cycle 3  10400       117535420.
##  5 cycle 4  10400       120746390.
##  6 cycle 5  10400        87088931.
##  7 cycle 6  10400        64260350.
##  8 cycle 7  10400        26612207.
##  9 cycle 8  10400         5610983.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       139573270.
##  4 cycle 3  10400       117218955.
##  5 cycle 4  10400       119812669.
##  6 cycle 5  10400        84922221.
##  7 cycle 6  10400        62537191.
##  8 cycle 7  10400        26113220.
##  9 cycle 8  10400         5559249.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       140579707.
##  4 cycle 3  10400       117582007.
##  5 cycle 4  10400       120072068.
##  6 cycle 5  10400        86052530.
##  7 cycle 6  10400        63325737.
##  8 cycle 7  10400        26361929.
##  9 cycle 8  10400         5335143.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220765256.
##  3 cycle 2  10400       139826696.
##  4 cycle 3  10400       117166360.
##  5 cycle 4  10400       118805831.
##  6 cycle 5  10400        84406592.
##  7 cycle 6  10400        62307969.
##  8 cycle 7  10400        25583539.
##  9 cycle 8  10400         5208452.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220420647.
##  3 cycle 2  10400       139212496.
##  4 cycle 3  10400       116813863.
##  5 cycle 4  10400       119660894.
##  6 cycle 5  10400        85872368.
##  7 cycle 6  10400        63623708.
##  8 cycle 7  10400        26411735.
##  9 cycle 8  10400         5722193.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       141645111.
##  4 cycle 3  10400       116692300.
##  5 cycle 4  10400       120144469.
##  6 cycle 5  10400        85810765.
##  7 cycle 6  10400        62504660.
##  8 cycle 7  10400        25358009.
##  9 cycle 8  10400         5294249.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       139272099.
##  4 cycle 3  10400       116536707.
##  5 cycle 4  10400       120276696.
##  6 cycle 5  10400        86314083.
##  7 cycle 6  10400        63228636.
##  8 cycle 7  10400        26392316.
##  9 cycle 8  10400         5837376.
## 10 cycle 9  10400          365675.
## 11 cycle 10 10400          177743.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       139959655.
##  4 cycle 3  10400       117887188.
##  5 cycle 4  10400       120624380.
##  6 cycle 5  10400        86255261.
##  7 cycle 6  10400        62702856.
##  8 cycle 7  10400        25997638.
##  9 cycle 8  10400         5682106.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221368322.
##  3 cycle 2  10400       139375020.
##  4 cycle 3  10400       116863910.
##  5 cycle 4  10400       120608117.
##  6 cycle 5  10400        87260497.
##  7 cycle 6  10400        63925917.
##  8 cycle 7  10400        26833665.
##  9 cycle 8  10400         5689040.
## 10 cycle 9  10400          346429.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       139909541.
##  4 cycle 3  10400       117443519.
##  5 cycle 4  10400       120508120.
##  6 cycle 5  10400        85615249.
##  7 cycle 6  10400        61977191.
##  8 cycle 7  10400        25634596.
##  9 cycle 8  10400         5694324.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       140689741.
##  4 cycle 3  10400       118118122.
##  5 cycle 4  10400       120882978.
##  6 cycle 5  10400        85660817.
##  7 cycle 6  10400        62628733.
##  8 cycle 7  10400        25725434.
##  9 cycle 8  10400         5149413.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       139711446.
##  4 cycle 3  10400       117043161.
##  5 cycle 4  10400       119886862.
##  6 cycle 5  10400        86713720.
##  7 cycle 6  10400        63943796.
##  8 cycle 7  10400        26451518.
##  9 cycle 8  10400         5736667.
## 10 cycle 9  10400          373373.
## 11 cycle 10 10400          177743.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       139316524.
##  4 cycle 3  10400       116633521.
##  5 cycle 4  10400       120809186.
##  6 cycle 5  10400        86429617.
##  7 cycle 6  10400        62660035.
##  8 cycle 7  10400        25667484.
##  9 cycle 8  10400         5115314.
## 10 cycle 9  10400          327183.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221540627.
##  3 cycle 2  10400       138924605.
##  4 cycle 3  10400       115993676.
##  5 cycle 4  10400       119523716.
##  6 cycle 5  10400        85622213.
##  7 cycle 6  10400        63593634.
##  8 cycle 7  10400        26561147.
##  9 cycle 8  10400         5931284.
## 10 cycle 9  10400          361825.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       139222457.
##  4 cycle 3  10400       117312492.
##  5 cycle 4  10400       120959360.
##  6 cycle 5  10400        86205512.
##  7 cycle 6  10400        63651539.
##  8 cycle 7  10400        26601870.
##  9 cycle 8  10400         5659284.
## 10 cycle 9  10400          377222.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       140387462.
##  4 cycle 3  10400       117983457.
##  5 cycle 4  10400       120853423.
##  6 cycle 5  10400        86186676.
##  7 cycle 6  10400        64761954.
##  8 cycle 7  10400        26895058.
##  9 cycle 8  10400         5961040.
## 10 cycle 9  10400          354127.
## 11 cycle 10 10400          172189.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       138436406.
##  4 cycle 3  10400       115513975.
##  5 cycle 4  10400       120490278.
##  6 cycle 5  10400        86267806.
##  7 cycle 6  10400        63438842.
##  8 cycle 7  10400        26779786.
##  9 cycle 8  10400         5919806.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       139696268.
##  4 cycle 3  10400       116437345.
##  5 cycle 4  10400       120214996.
##  6 cycle 5  10400        84807153.
##  7 cycle 6  10400        61897354.
##  8 cycle 7  10400        25514940.
##  9 cycle 8  10400         5256819.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       139448059.
##  4 cycle 3  10400       117277734.
##  5 cycle 4  10400       119823771.
##  6 cycle 5  10400        85153317.
##  7 cycle 6  10400        62240972.
##  8 cycle 7  10400        25705701.
##  9 cycle 8  10400         5256079.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       140267467.
##  4 cycle 3  10400       117610941.
##  5 cycle 4  10400       120954305.
##  6 cycle 5  10400        86597001.
##  7 cycle 6  10400        63816743.
##  8 cycle 7  10400        26253550.
##  9 cycle 8  10400         5630808.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            8248.
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       139427665.
##  4 cycle 3  10400       117257169.
##  5 cycle 4  10400       120052814.
##  6 cycle 5  10400        85234214.
##  7 cycle 6  10400        62665287.
##  8 cycle 7  10400        25971950.
##  9 cycle 8  10400         5395698.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       139287593.
##  4 cycle 3  10400       117705027.
##  5 cycle 4  10400       120500389.
##  6 cycle 5  10400        84674399.
##  7 cycle 6  10400        63291425.
##  8 cycle 7  10400        26603751.
##  9 cycle 8  10400         5736971.
## 10 cycle 9  10400          400318.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            8248.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221389860.
##  3 cycle 2  10400       139052820.
##  4 cycle 3  10400       117396203.
##  5 cycle 4  10400       119805718.
##  6 cycle 5  10400        85848893.
##  7 cycle 6  10400        62705099.
##  8 cycle 7  10400        25833502.
##  9 cycle 8  10400         5410609.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       139231783.
##  4 cycle 3  10400       116621509.
##  5 cycle 4  10400       118936373.
##  6 cycle 5  10400        84360091.
##  7 cycle 6  10400        62690385.
##  8 cycle 7  10400        26262949.
##  9 cycle 8  10400         5771270.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       140386356.
##  4 cycle 3  10400       117980363.
##  5 cycle 4  10400       120203494.
##  6 cycle 5  10400        85617106.
##  7 cycle 6  10400        62950141.
##  8 cycle 7  10400        26714949.
##  9 cycle 8  10400         5486646.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       139543233.
##  4 cycle 3  10400       117810210.
##  5 cycle 4  10400       120532029.
##  6 cycle 5  10400        85973258.
##  7 cycle 6  10400        63729133.
##  8 cycle 7  10400        26263888.
##  9 cycle 8  10400         5563052.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          172189.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       139153210.
##  4 cycle 3  10400       116635341.
##  5 cycle 4  10400       118847498.
##  6 cycle 5  10400        84414022.
##  7 cycle 6  10400        61773957.
##  8 cycle 7  10400        25630835.
##  9 cycle 8  10400         5255842.
## 10 cycle 9  10400          230952.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       139219136.
##  4 cycle 3  10400       116088670.
##  5 cycle 4  10400       120055300.
##  6 cycle 5  10400        85625470.
##  7 cycle 6  10400        63759023.
##  8 cycle 7  10400        26373834.
##  9 cycle 8  10400         5657567.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221196018.
##  3 cycle 2  10400       139875549.
##  4 cycle 3  10400       117103942.
##  5 cycle 4  10400       119931099.
##  6 cycle 5  10400        85528537.
##  7 cycle 6  10400        63243166.
##  8 cycle 7  10400        25708833.
##  9 cycle 8  10400         5497687.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       140087397.
##  4 cycle 3  10400       117009676.
##  5 cycle 4  10400       120341578.
##  6 cycle 5  10400        86042776.
##  7 cycle 6  10400        63883740.
##  8 cycle 7  10400        26231625.
##  9 cycle 8  10400         5528686.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       139873652.
##  4 cycle 3  10400       117545429.
##  5 cycle 4  10400       120765538.
##  6 cycle 5  10400        86593519.
##  7 cycle 6  10400        63094641.
##  8 cycle 7  10400        25954723.
##  9 cycle 8  10400         5341943.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       139036536.
##  4 cycle 3  10400       116174201.
##  5 cycle 4  10400       120005207.
##  6 cycle 5  10400        85389743.
##  7 cycle 6  10400        63135220.
##  8 cycle 7  10400        26333114.
##  9 cycle 8  10400         5908869.
## 10 cycle 9  10400          369524.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       139861478.
##  4 cycle 3  10400       117132513.
##  5 cycle 4  10400       120231869.
##  6 cycle 5  10400        85323249.
##  7 cycle 6  10400        62596416.
##  8 cycle 7  10400        26070620.
##  9 cycle 8  10400         5573553.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       139825117.
##  4 cycle 3  10400       117019140.
##  5 cycle 4  10400       120804720.
##  6 cycle 5  10400        86868307.
##  7 cycle 6  10400        63736721.
##  8 cycle 7  10400        26692081.
##  9 cycle 8  10400         5422457.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            8248.
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       140485639.
##  4 cycle 3  10400       118181269.
##  5 cycle 4  10400       120166188.
##  6 cycle 5  10400        85605952.
##  7 cycle 6  10400        63403394.
##  8 cycle 7  10400        26045876.
##  9 cycle 8  10400         5729363.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       140335924.
##  4 cycle 3  10400       117415131.
##  5 cycle 4  10400       120877332.
##  6 cycle 5  10400        85952574.
##  7 cycle 6  10400        63103918.
##  8 cycle 7  10400        26051828.
##  9 cycle 8  10400         5671368.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       139695162.
##  4 cycle 3  10400       116757632.
##  5 cycle 4  10400       119628664.
##  6 cycle 5  10400        84998003.
##  7 cycle 6  10400        63482033.
##  8 cycle 7  10400        26498815.
##  9 cycle 8  10400         5390481.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       140316003.
##  4 cycle 3  10400       116745803.
##  5 cycle 4  10400       119626073.
##  6 cycle 5  10400        85184931.
##  7 cycle 6  10400        62376287.
##  8 cycle 7  10400        25729506.
##  9 cycle 8  10400         5300946.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       141706928.
##  4 cycle 3  10400       116911043.
##  5 cycle 4  10400       119878435.
##  6 cycle 5  10400        85087281.
##  7 cycle 6  10400        61909825.
##  8 cycle 7  10400        25940000.
##  9 cycle 8  10400         5563422.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220506799.
##  3 cycle 2  10400       140244860.
##  4 cycle 3  10400       118288638.
##  5 cycle 4  10400       120494533.
##  6 cycle 5  10400        86207836.
##  7 cycle 6  10400        63111598.
##  8 cycle 7  10400        26313066.
##  9 cycle 8  10400         5360592.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       139028788.
##  4 cycle 3  10400       117134878.
##  5 cycle 4  10400       120518337.
##  6 cycle 5  10400        85185398.
##  7 cycle 6  10400        61828483.
##  8 cycle 7  10400        25802178.
##  9 cycle 8  10400         5581901.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       139130920.
##  4 cycle 3  10400       116764003.
##  5 cycle 4  10400       120774765.
##  6 cycle 5  10400        86731371.
##  7 cycle 6  10400        63520554.
##  8 cycle 7  10400        26085968.
##  9 cycle 8  10400         5103096.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       139469877.
##  4 cycle 3  10400       116369286.
##  5 cycle 4  10400       120752646.
##  6 cycle 5  10400        85980697.
##  7 cycle 6  10400        63524947.
##  8 cycle 7  10400        26427083.
##  9 cycle 8  10400         5473955.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       140089611.
##  4 cycle 3  10400       117245524.
##  5 cycle 4  10400       120636282.
##  6 cycle 5  10400        86419396.
##  7 cycle 6  10400        63841288.
##  8 cycle 7  10400        26049948.
##  9 cycle 8  10400         5533903.
## 10 cycle 9  10400          377222.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       139716979.
##  4 cycle 3  10400       116926328.
##  5 cycle 4  10400       119780924.
##  6 cycle 5  10400        85867020.
##  7 cycle 6  10400        62381909.
##  8 cycle 7  10400        25472967.
##  9 cycle 8  10400         5316834.
## 10 cycle 9  10400          334881.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220657566.
##  3 cycle 2  10400       139547343.
##  4 cycle 3  10400       117459533.
##  5 cycle 4  10400       119775468.
##  6 cycle 5  10400        85303256.
##  7 cycle 6  10400        62409279.
##  8 cycle 7  10400        25573514.
##  9 cycle 8  10400         5167892.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       141035338.
##  4 cycle 3  10400       117996014.
##  5 cycle 4  10400       121059358.
##  6 cycle 5  10400        86578399.
##  7 cycle 6  10400        64171051.
##  8 cycle 7  10400        26288005.
##  9 cycle 8  10400         5528819.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       139053137.
##  4 cycle 3  10400       116595124.
##  5 cycle 4  10400       119099756.
##  6 cycle 5  10400        85621055.
##  7 cycle 6  10400        63879347.
##  8 cycle 7  10400        26484717.
##  9 cycle 8  10400         5282971.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            8248.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       139513668.
##  4 cycle 3  10400       116612228.
##  5 cycle 4  10400       119743027.
##  6 cycle 5  10400        84904561.
##  7 cycle 6  10400        62411983.
##  8 cycle 7  10400        25737964.
##  9 cycle 8  10400         5293576.
## 10 cycle 9  10400          346429.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       140034435.
##  4 cycle 3  10400       117703387.
##  5 cycle 4  10400       119613476.
##  6 cycle 5  10400        85097286.
##  7 cycle 6  10400        62404118.
##  8 cycle 7  10400        25610477.
##  9 cycle 8  10400         5613373.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       139947009.
##  4 cycle 3  10400       116968185.
##  5 cycle 4  10400       120697392.
##  6 cycle 5  10400        85592689.
##  7 cycle 6  10400        63875784.
##  8 cycle 7  10400        26881276.
##  9 cycle 8  10400         5785035.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0

The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:

#Males
tot_discounted_costs_m_alt <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m_alt[[i]]$discounted_costs) 
tot_discounted_costs_m_alt[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m_alt)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1406310008
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1397889819
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1401114707
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1403868161
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1399483780
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1405879236
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1407716845
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1403795688
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1403620102
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1413842175
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1408141134
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1406786241
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1405113784
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1401769306
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1402112637
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1402950910
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1400206658
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1399648404
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1401070528
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1403362294
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1400876309
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1409619742
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1404086648
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1404516872
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1401827130
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1414601677
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1396989073
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1395469547
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1406727732
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1402785084
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1401091029
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1400831762
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1406714036
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1407868072
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1406355331
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1408188211
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1395509637
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1407455890
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1407548263
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1400269077
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1409999228
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1401103595
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1407489169
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1398609465
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1398752301
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1401692284
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1402734411
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1402428490
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1403119458
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1396518603
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1407408375
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1405123007
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1408423912
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1400371970
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1402561737
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1405712421
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1404434516
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1408921331
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1398302935
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1395893111
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1405936796
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1402051717
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1402634294
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1400444371
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1406660677
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1402220553
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1406306031
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1404872726
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1395226091
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1409158472
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1397514161
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1408596097
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1402582364
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1403622060
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1404448553
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1399565668
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1403556787
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1405017685
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1403231551
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1393444742
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1398211638
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1408188476
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1400351238
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1406113303
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1403810873
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1404177437
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1403845397
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1402530749
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1404018726
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1402434578
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1398720502
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1406461844
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1407554573
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1401867641
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1402957957
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1402060982
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1404395032
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1403303511
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1400958163
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1408081599
#Females
tot_discounted_costs_f_alt <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f_alt[[i]]$discounted_costs) 
tot_discounted_costs_f_alt[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f_alt)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1043571341
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1037696990
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1040451871
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1040362973
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1037748016
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1042336789
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1040677833
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1042927936
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1038040702
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1046244734
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1040256802
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1040333927
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1037420107
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1040859439
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1040814150
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1044881135
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1040315010
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1040145960
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1040792175
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1040282924
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1042277852
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1042308085
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1035126967
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1038460162
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1036808046
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1041002440
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1037280617
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1044435875
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1039391256
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1041052098
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1034339484
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1038065107
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1042981020
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1041459939
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1040952767
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1036049508
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1044122632
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1040647721
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1037504741
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1044358230
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1040945221
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1042413062
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1035581727
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1038665626
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1039043020
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1038010426
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1036544973
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1041772257
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1045244449
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1043845966
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1038553748
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1042218526
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1035786439
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1039519017
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1040091717
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1040545673
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1041829049
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1043708417
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1039141838
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1041343818
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1042218893
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1039709431
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1039536403
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1042225655
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1046211830
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1039694454
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1036401035
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1037322692
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1043965067
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1038520651
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1040856182
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1039222529
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1036814992
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1042057955
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1042163986
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1034249545
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1039456138
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1039859814
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1042007846
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1041782349
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1038698150
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1039646130
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1043041620
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1042355089
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1042282468
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1038870857
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1038058401
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1039777123
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1042313297
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1037811003
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1040666818
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1040760224
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1042761042
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1038081478
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1037648321
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1045606470
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1038647128
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1036543179
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1039250487
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1042243481
#Averaging total costs across simulations
TDC_m_alternative <- mean(unlist(tot_discounted_costs_m_alt))
TDC_f_alternative <- mean(unlist(tot_discounted_costs_f_alt))
#Final result
TDC_alternative <- TDC_m_alternative + TDC_f_alternative
TDC_alternative
## [1] 2443696800

The total amount of money that needs to be invested for early detection is:

total_savings <- TDC_baseline - TDC_alternative
total_savings
## [1] -319038206

The following is a useful graph to evaluate the trends of P, MPD, APD and D patients over the microsimulation time period:

prepare_plot_data <- function(df_m, scenario) {
  df_m %>%
    as_tibble() %>%
    pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
    count(cycle, state) %>%
    group_by(cycle) %>%
    mutate(percent = n / sum(n)) %>%
    ungroup() %>%
    mutate(scenario = scenario)
}


num_cols_m <- ncol(model_results_m[[50]])
num_cols_m_alt <- ncol(model_results_m_alt[[50]])

colnames(model_results_m[[50]]) <- paste("cycle", 0:(num_cols_m-1), sep = " ")
colnames(model_results_m_alt[[50]]) <- paste("cycle", 0:(num_cols_m_alt-1), sep = " ")


# Baseline
df_m.M <- model_results_m[[50]] %>% prepare_plot_data("Baseline")

# Alternative
df_m.M_alt <- model_results_m_alt[[50]] %>% prepare_plot_data("Alternative")

# Combining
combined_data_m <- bind_rows(df_m.M, df_m.M_alt)

combined_data1 <- combined_data_m %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% 
filter(cycle != "cycle 15")

# Plot 
summary_plot_male <- ggplot(combined_data1 %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
  geom_line() +
  labs(title = "Comparison of states across cycles and scenarios (Males)",
       x = "Cycle",
       y = "Percentage") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_male

The same graph as before for females:

prepare_plot_data <- function(df_m, scenario) {
  df_m %>%
    as_tibble() %>%
    pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
    count(cycle, state) %>%
    group_by(cycle) %>%
    mutate(percent = n / sum(n)) %>%
    ungroup() %>%
    mutate(scenario = scenario)
}


num_cols_f <- ncol(model_results_f[[50]])
num_cols_f_alt <- ncol(model_results_f_alt[[50]])

colnames(model_results_f[[50]]) <- paste("cycle", 0:(num_cols_f-1), sep = " ")
colnames(model_results_f_alt[[50]]) <- paste("cycle", 0:(num_cols_f_alt-1), sep = " ")


# Baseline
df_m.M <- model_results_f[[50]] %>% prepare_plot_data("Baseline")

# Alternative
df_m.M_alt <- model_results_f_alt[[50]] %>% prepare_plot_data("Alternative")

# Combining
combined_data_f <- bind_rows(df_m.M, df_m.M_alt)

combined_data2 <- combined_data_f %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% 
filter(cycle != "cycle 15")

# Plot 
summary_plot_female <- ggplot(combined_data2 %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
  geom_line() +
  labs(title = "Comparison of states across cycles and scenarios (Females)",
       x = "Cycle",
       y = "Percentage") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_female

Losses are really prominent from a financial point of view, as indicated by the final result and by the graph comparing costs across scenarios:

However, if the point of view of patients is considered, the previous remarks represent a gain both in terms of life quality and life expectancy.

Let’s evaluate this gain:

process_model_result <- function(model_result) {
  df <- model_result %>% as_tibble()
  cycle_columns <- paste0("cycle ", 0:14)
  map(cycle_columns, ~ df %>% tabyl(!!sym(.x)))
}

# Males
percent_tables_m <- map(model_results_m[1:100], process_model_result)

# Females
percent_tables_f <- map(model_results_f[1:100], process_model_result)

# Aggregate results and compute the averages
aggregate_results <- function(percent_tables) {
  all_states <- c("P", "MPD", "APD", "D")
  cycle_columns <- paste0("cycle ", 0:14)
  
  aggregated <- map(cycle_columns, function(cycle) {
    state_sums <- map_dbl(all_states, function(state) {
      state_n_values <- map_dbl(percent_tables, ~ {
        tabyl_result <- .x[[which(cycle_columns == cycle)]]
        if (state %in% tabyl_result[[1]]) {
          return(tabyl_result$n[tabyl_result[[1]] == state])
        } else {
          return(0)
        }
      })
      mean(state_n_values)
    })
    tibble(state = all_states, mean_n = state_sums)
  })
  
  bind_rows(aggregated, .id = "cycle") %>%
    mutate(cycle = as.numeric(cycle) - 1) # Aggiustare i cicli da 0 a 14
}

# Aggregate for males
aggregated_m <- aggregate_results(percent_tables_m)

# Aggregate for females
aggregated_f <- aggregate_results(percent_tables_f)

aggregated_m
aggregated_f
#Same approach for the alternative scenario

percent_tables_m_alt <- map(model_results_m_alt[1:100], process_model_result)
percent_tables_f_alt <- map(model_results_f_alt[1:100], process_model_result)

# Aggregate for males
aggregated_m_alt <- aggregate_results(percent_tables_m_alt)

# Aggregate for females
aggregated_f_alt <- aggregate_results(percent_tables_f_alt)

aggregated_m_alt
aggregated_f_alt

With the new tables at hand it is possible to compute the 3 differences that indicate a gain for patients:

library(dplyr)

calculate_differences <- function(baseline, alternative) {
  baseline %>%
    inner_join(alternative, by = c("cycle", "state"), suffix = c("_baseline", "_alt")) %>%
    mutate(
      difference = case_when(
        state == "MPD" ~ mean_n_alt - mean_n_baseline,
        state == "APD" ~ mean_n_baseline - mean_n_alt,
        state == "D" ~ mean_n_baseline - mean_n_alt,
        TRUE ~ NA_real_
      )
    ) %>%
    select(cycle, state, difference) %>%
    filter(!is.na(difference))
}

differences_m <- calculate_differences(aggregated_m, aggregated_m_alt)
differences_f <- calculate_differences(aggregated_f, aggregated_f_alt)

differences_m
differences_f

Differences are aggregated with respect to cycles, truncated, since patients have to be counted with integer numbers, and multiplied by 5, since each cycle lasts 5 years.

#Males
summary_m <- differences_m %>% 
    group_by(state) %>%
    summarise(
      diff_sum = sum(difference, na.rm = TRUE)
    ) %>%
    mutate(
      diff_sum = floor(diff_sum) * 5
    ) %>%
    select(state, diff_sum)
summary_m
#Females
summary_f <- differences_f %>% 
    group_by(state) %>%
    summarise(
      diff_sum = sum(difference, na.rm = TRUE)
    ) %>%
    mutate(
      diff_sum = floor(diff_sum) * 5
    ) %>%
    select(state, diff_sum)
summary_f

The previous are the total numbers of years:

The results with respect to the average male or female patient require the previous results to be divided by the total number of male and females patients:

averages_m <- summary_m %>%
    mutate(
      diff_sum = (diff_sum)/(n_males)
    ) %>%
    select(state, diff_sum)

averages_f <- summary_f %>%
    mutate(
      diff_sum = (diff_sum)/(n_females)
    ) %>%
    select(state, diff_sum)

averages_m
averages_f

Therefore, in alternative scenario A1 a male patient gains, on average, about 2 years and 1 month more in the mild stage, about 4 months and a half less in the severe stage and about 1 year, as well as about 1 year and 9 months in terms of life expectancy. In the same way, a female patient gains, on average, about 2 years and 3 months more in the mild stage, about 4 months and a half less in the severe stage and about 1 year and 11 months in terms of life expectancy. Note that the subdivision of gains is compliant with the methodological framework suggested by the LEMEREND project: there are gains in terms of years in almost “perfect health” referring to the MPD stage and gains in the severity of the illness referring to the APD stage. The first set of benefits considers the MPD stage instead of the prodromal state P since state-of-the-art detection techniques do not allow physicians to identify prodromal patients. However, this analysis can be improved in the future with the data obtained from the new AI-supported algorithms for early detection which, hopefully, will be put in place.

Alternative scenario: A2

This alternative scenario does not consider a gain in terms of life expectancy but it redistributes the positive gain in P(MPD→MPD) only to P(MPD→APD) by adopting the same approach as alternative scenario A1:

\[ p'(\mathrm{MPD} \rightarrow \mathrm{APD})\ =\ 1\ -\ \ p'(\mathrm{MPD} \rightarrow \mathrm{MPD}) - \ p(\mathrm{MPD} \rightarrow \mathrm{D})\ \]

\[ \mathrm{\Delta}\ =\ p^\prime\ -\ p \]

This gain is counterbalanced by a proportional redistribution of its negative value, - delta, with respect to the probability of transitioning to APD:

\[ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{APD})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \]

\[ p'(\mathrm{MPD} \rightarrow \mathrm{APD}) = p(\mathrm{MPD} \rightarrow \mathrm{APD}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) \]

The probability of dying when MPD, P(MPD→D), is the same of the baseline scenario, which causes probabilities of transitioning from MPD to sum up to a number higher than 1. This issue is tackled by dividing probabilities by their sum if their sum is more than 1. However, this adjustment artificially reduces the probability of dying, hence the consequent moderate gain in terms of life expectancy shall not be considered. However, this adjustment will artificially decrease P(MPD→MPD) and P(MPD→APD) as well, but a lower probability of remaining MPD can be assumed to be counterbalanced by a lower probability of transitioning to another state, which is APD.

(Note1: j’ai essayé à computer P(MPD→APD) comme P’(MPD→APD) = 1 - P’(MPD→MPD) - P(MPD→D), mais il y etait des probabilités negatives. Le meme problème concerne le calcul avec l’approche concernant la division de delta entre les deux probabilités mais sans le facteur de ponderation (p(MPD→APD)/p(MPD→D)+p(MPD→APD)) au face de delta, donc j’ai du utiliser la meme formule que j’ai utilisé pour calculer dans le scenario alternatif A1)

# Adjust probability_of_death for 95+ patients
final_data2_alt <- final_data_alt_old %>%
  group_by(gender) %>%
  mutate(probability_of_death = ifelse(
    age_class == "95et+" & severity == "Prodromal",
    probability_of_death[age_class == "95et+" & severity == "Mild"] -
      (probability_of_death[age_class == "90-94" & severity == "Mild"] -
       probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
    probability_of_death
  ))

age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")

# Update f_prob1 with correct probabilities
f_prob2 <- f_prob %>%
  mutate(
    F = case_when(
      `Age class` == "95et+" & Gender == "Male" ~ final_data1_alt %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      `Age class` == "95et+" & Gender == "Female" ~ final_data1_alt %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      TRUE ~ F
    )
  )

# Function to generate transition matrix
generate_transition_matrix_alt2 <- function(summary_df, summary_df2, final_data2_alt, age_class, gender_name) {
  x <- matrix(NA, nrow = 4, ncol = 4)
  x[1, 1] <- 0

  f_prob2 <- f_prob1 %>%
    filter(`Age class` == age_class & Gender == gender_name) %>%
    pull(F)
   
  x[1, 2] <- 1 - f_prob2
  x[1, 3] <- 0
  x[1, 4] <- f_prob2

  x[2, 1] <- 0

  numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

   x[2, 2] <- (numerator_MPD_MPD / denominator_MPD)^(4/5)  
  
   x[2, 3] <- 1 - ((numerator_MPD_MPD / denominator_MPD)^(4/5)) - (numerator_MPD_D / denominator_MPD) 
   
   x[2, 4] <- numerator_MPD_D / denominator_MPD
   
  
  #The adjustment is introduced here
  #sum_probs <- x[2, 2] + x[2, 3] + x[2, 4]

  #if (sum_probs > 1) {
  #x[2, 2] <- x[2, 2] / sum_values
  #x[2, 3] <- x[2, 3] / sum_values
  #x[2, 4] <- x[2, 4] / sum_values
  #}
   
  
  x[3, 1] <- 0
  x[3, 2] <- 0
  numerator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)
  
  denominator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

    x[3, 4] <- numerator_APD_D / denominator_APD_D
    x[3, 3] <- 1 - x[3, 4]

  x[4, 1] <- 0
  x[4, 2] <- 0
  x[4, 3] <- 0
  x[4, 4] <- 1

  return(x)
}

transition_matrices_alt2 <- list()

for (gender in genders) {
  for (age_class in age_classes) {
    matrix_name <- paste(gender, age_class, sep = "_")
    transition_matrices_alt2[[matrix_name]] <- generate_transition_matrix_alt2(summary_df, summary_df2, final_data2_alt, age_class, gender)
  }
}

names(transition_matrices_alt2) <- NULL  

males_alt2 <- transition_matrices_alt2[1:10]
females_alt2 <- transition_matrices_alt2[11:20]

matrices_mf_alt2 <- list(males_alt2, females_alt2)

for (i in 1:length(males_alt2)) {
  colnames(males_alt2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  rownames(males_alt2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt2)) {
  colnames(females_alt2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_alt2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}

transition_matrices_m_alt2 <- matrices_mf_alt2[[1]]
transition_matrices_f_alt2 <- matrices_mf_alt2[[2]]

extract_rows_as_named_list <- function(matrix) {
  list(
    P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
    MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
    APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
    D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
  )
}

transition_prob_m_alt2 <- lapply(transition_matrices_m_alt2, extract_rows_as_named_list)
transition_prob_f_alt2 <- lapply(transition_matrices_f_alt2, extract_rows_as_named_list)

print("Transition Probabilities for Males:")
## [1] "Transition Probabilities for Males:"
print(transition_prob_m_alt2)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8717192 0.0782808 0.0500000 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87555133 0.05506091 0.06938776 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.8594377 0.0355623 0.1050000 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.79601832 0.02388092 0.18010076 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.756888296 0.005142376 0.237969328 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.68577684 -0.01200958  0.32623274 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.58180832 -0.03967971  0.45787140 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.43477009 -0.06089622  0.62612613 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.31475697 -0.06792152  0.75316456 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2304007 0.0000000 0.7695993 
## 
## [[10]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.22057481 -0.06941202  0.84883721 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Transition Probabilities for Females:")
## [1] "Transition Probabilities for Females:"
print(transition_prob_f_alt2)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92266989 0.04762714 0.02970297 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92675864 0.02207857 0.05116279 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91266120 0.03904335 0.04829545 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.87363982 0.01892216 0.10743802 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.83033033 0.02067638 0.14899329 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.768893159 0.001606841 0.229500000 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.69217160 -0.02698224  0.33481064 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.57092958 -0.05580811  0.48487853 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.41639271 -0.07613896  0.65974625 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3949789 0.0000000 0.6050211 
## 
## [[10]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.3727036 -0.0760003  0.7032967 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
# Function to calculate delta
calculate_delta <- function(baseline, alt) {
  delta <- alt - baseline
  return(delta)
}

update_transition_probabilities1 <- function(transition_prob_m, transition_prob_f, transition_prob_m_alt2, transition_prob_f_alt2) {
  for (i in 1:length(transition_prob_m)) {
    # Extract baseline and alternative matrices
    baseline_matrix_m <- transition_prob_m[[i]]$MPD
    alt_matrix_m <- transition_prob_m_alt2[[i]]$MPD
    baseline_matrix_f <- transition_prob_f[[i]]$MPD
    alt_matrix_f <- transition_prob_f_alt2[[i]]$MPD
    
    # Baseline and alternative [2,2] elements
    baseline_m_MPD <- baseline_matrix_m["MPD"]
    alt_m_MPD <- alt_matrix_m["MPD"]
    
    baseline_f_MPD <- baseline_matrix_f["MPD"]
    alt_f_MPD <- alt_matrix_f["MPD"]
    
    # Calculate deltas
    delta_m <- calculate_delta(baseline_m_MPD, alt_m_MPD)
    delta_f <- calculate_delta(baseline_f_MPD, alt_f_MPD)
    
    # Calculate baseline probabilities
    p_m_APD <- baseline_matrix_m["APD"]
    p_m_D <- baseline_matrix_m["D"]
    p_f_APD <- baseline_matrix_f["APD"]
    p_f_D <- baseline_matrix_f["D"]
    
    # Calculate delta distribution for males
    sum_m_APD_D <- p_m_APD + p_m_D
    delta_m_APD <- (p_m_APD / sum_m_APD_D) * delta_m
    
    # Calculate delta distribution for females
    sum_f_APD_D <- p_f_APD + p_f_D
    delta_f_APD <- (p_f_APD / sum_f_APD_D) * delta_f
    
    
    # Update alternative transition probabilities for males
    transition_prob_m_alt2[[i]]$MPD["APD"] <- baseline_matrix_m["APD"] - delta_m_APD
    
    # Update alternative transition probabilities for females
    transition_prob_f_alt2[[i]]$MPD["APD"] <- baseline_matrix_f["APD"] - delta_f_APD
    
    #Adjust probabilities if their sum is more than 1
    sum_probs_m <- transition_prob_m_alt2[[i]]$MPD["MPD"] + transition_prob_m_alt2[[i]]$MPD["APD"] + transition_prob_m_alt2[[i]]$MPD["D"]  

  if (sum_probs_m > 1) {
  transition_prob_m_alt2[[i]]$MPD["MPD"] <- transition_prob_m_alt2[[i]]$MPD["MPD"] / sum_probs_m
  transition_prob_m_alt2[[i]]$MPD["APD"] <- transition_prob_m_alt2[[i]]$MPD["APD"] / sum_probs_m
  transition_prob_m_alt2[[i]]$MPD["D"]   <- transition_prob_m_alt2[[i]]$MPD["D"]   / sum_probs_m
  }
    
  sum_probs_f <- transition_prob_f_alt2[[i]]$MPD["MPD"] + transition_prob_f_alt2[[i]]$MPD["APD"] + transition_prob_f_alt2[[i]]$MPD["D"]  

  if (sum_probs_f > 1) {
  transition_prob_f_alt2[[i]]$MPD["MPD"] <- transition_prob_f_alt2[[i]]$MPD["MPD"] / sum_probs_f
  transition_prob_f_alt2[[i]]$MPD["APD"] <- transition_prob_f_alt2[[i]]$MPD["APD"] / sum_probs_f
  transition_prob_f_alt2[[i]]$MPD["D"]   <- transition_prob_f_alt2[[i]]$MPD["D"]   / sum_probs_f
  } 
    
  }
  return(list(transition_prob_m_alt2, transition_prob_f_alt2))
}

# Call the function to update transition probabilities
updated_transition_probs1 <- update_transition_probabilities1(transition_prob_m, transition_prob_f, transition_prob_m_alt2, transition_prob_f_alt2)
transition_prob_m_altA <- updated_transition_probs1[[1]]
transition_prob_f_altA <- updated_transition_probs1[[2]]

print("Updated Transition Probabilities for Males (Alternative Scenario):")
## [1] "Updated Transition Probabilities for Males (Alternative Scenario):"
print(transition_prob_m_altA)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.86366501 0.08679696 0.04953803 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.86433996 0.06716079 0.06849925 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.84304855 0.05395376 0.10299769 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.77131104 0.05417828 0.17451068 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.72693406 0.04451436 0.22855157 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.65091741 0.03943292 0.30964967 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.54446584 0.02705054 0.42848362 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.40290151 0.01686732 0.58023117 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.292022279 0.009213745 0.698763977 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2304007 0.0000000 0.7695993 
## 
## [[10]]$MPD
##        P      MPD      APD        D 
## 0.000000 0.206258 0.000000 0.793742 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Updated Transition Probabilities for Females (Alternative Scenario):")
## [1] "Updated Transition Probabilities for Females (Alternative Scenario):"
print(transition_prob_f_altA)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91743686 0.05302864 0.02953451 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91772169 0.03161441 0.05066390 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.9043208 0.0478251 0.0478541 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.85645816 0.03821678 0.10532506 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.80842627 0.04651087 0.14506286 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.73926708 0.04007574 0.22065718 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.65593643 0.02678026 0.31728331 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.53265510 0.01497208 0.45237282 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.385122243 0.004677463 0.610200294 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3949789 0.0000000 0.6050211 
## 
## [[10]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.344820367 0.004498963 0.650680671 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
males_altA <- lapply(transition_prob_m_altA, function(prob) {
  matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
females_altA <- lapply(transition_prob_f_altA, function(prob) {
  matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})

for (i in 1:length(males_altA)) {
  colnames(males_altA[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  rownames(males_altA[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_altA)) {
  colnames(females_altA[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_altA[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}

print("Updated Transition Matrices for Males (Alternative Scenario):")
## [1] "Updated Transition Matrices for Males (Alternative Scenario):"
print(males_altA)
## [[1]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9712352 0.00000000 0.02876483
## MPD.m   0 0.8636650 0.08679696 0.04953803
## APD.m   0 0.0000000 0.92913386 0.07086614
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[2]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9574518 0.00000000 0.04254822
## MPD.m   0 0.8643400 0.06716079 0.06849925
## APD.m   0 0.0000000 0.87280702 0.12719298
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[3]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9433756 0.00000000 0.05662437
## MPD.m   0 0.8430486 0.05395376 0.10299769
## APD.m   0 0.0000000 0.81914894 0.18085106
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[4]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9224868 0.00000000 0.07751319
## MPD.m   0 0.7713110 0.05417828 0.17451068
## APD.m   0 0.0000000 0.69558600 0.30441400
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[5]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8875735 0.00000000 0.1124265
## MPD.m   0 0.7269341 0.04451436 0.2285516
## APD.m   0 0.0000000 0.57037037 0.4296296
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[6]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8201575 0.00000000 0.1798425
## MPD.m   0 0.6509174 0.03943292 0.3096497
## APD.m   0 0.0000000 0.48199768 0.5180023
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[7]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.7046099 0.00000000 0.2953901
## MPD.m   0 0.5444658 0.02705054 0.4284836
## APD.m   0 0.0000000 0.33866995 0.6613300
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[8]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.5279737 0.00000000 0.4720263
## MPD.m   0 0.4029015 0.01686732 0.5802312
## APD.m   0 0.0000000 0.25708502 0.7429150
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[9]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.3260733 0.000000000 0.6739267
## MPD.m   0 0.2920223 0.009213745 0.6987640
## APD.m   0 0.0000000 0.160305344 0.8396947
## D.m     0 0.0000000 0.000000000 1.0000000
## 
## [[10]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.2304007 0.0000000 0.7695993
## MPD.m   0 0.2062580 0.0000000 0.7937420
## APD.m   0 0.0000000 0.1111111 0.8888889
## D.m     0 0.0000000 0.0000000 1.0000000
print("Updated Transition Matrices for Females (Alternative Scenario):")
## [1] "Updated Transition Matrices for Females (Alternative Scenario):"
print(females_altA)
## [[1]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9864538 0.00000000 0.01354618
## MPD.f   0 0.9174369 0.05302864 0.02953451
## APD.f   0 0.0000000 0.91935484 0.08064516
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9814785 0.00000000 0.01852146
## MPD.f   0 0.9177217 0.03161441 0.05066390
## APD.f   0 0.0000000 0.86885246 0.13114754
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[3]]
##       P.f     MPD.f     APD.f        D.f
## P.f     0 0.9750718 0.0000000 0.02492824
## MPD.f   0 0.9043208 0.0478251 0.04785410
## APD.f   0 0.0000000 0.8565401 0.14345992
## D.f     0 0.0000000 0.0000000 1.00000000
## 
## [[4]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9644648 0.00000000 0.03553525
## MPD.f   0 0.8564582 0.03821678 0.10532506
## APD.f   0 0.0000000 0.77889447 0.22110553
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[5]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9455591 0.00000000 0.05444087
## MPD.f   0 0.8084263 0.04651087 0.14506286
## APD.f   0 0.0000000 0.71125265 0.28874735
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[6]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9040836 0.00000000 0.09591642
## MPD.f   0 0.7392671 0.04007574 0.22065718
## APD.f   0 0.0000000 0.61809816 0.38190184
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[7]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.8160931 0.00000000 0.1839069
## MPD.f   0 0.6559364 0.02678026 0.3172833
## APD.f   0 0.0000000 0.48024316 0.5197568
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[8]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.6559712 0.00000000 0.3440288
## MPD.f   0 0.5326551 0.01497208 0.4523728
## APD.f   0 0.0000000 0.37564767 0.6243523
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[9]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.4385294 0.000000000 0.5614706
## MPD.f   0 0.3851222 0.004677463 0.6102003
## APD.f   0 0.0000000 0.268041237 0.7319588
## D.f     0 0.0000000 0.000000000 1.0000000
## 
## [[10]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.3949789 0.000000000 0.6050211
## MPD.f   0 0.3448204 0.004498963 0.6506807
## APD.f   0 0.0000000 0.222222222 0.7777778
## D.f     0 0.0000000 0.000000000 1.0000000

The graph showcasing probabilities of remaining MPD:

extract_probabilities2_alt <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2]
    ))
    
  }
  
  return(data)
}

males_data_rem_altA <- extract_probabilities2_alt(males_altA, age_classes, "Male")
females_data_rem_altA <- extract_probabilities2_alt(females_altA, age_classes, "Female")

final_data_rem_altA <- rbind(males_data_rem_altA, females_data_rem_altA)

graph_prob_mf_rem_altA <- ggplot(final_data_rem_altA, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD with respect to gender and age classes, alternative scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_altA

The graph showcasing probabilities of transitioning from MPD to APD is:

extract_probabilities1 <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3]
    ))
    
  }
  
  return(data)
}

males_data_tra_altA <- extract_probabilities1(males_altA, age_classes, "Male")
females_data_tra_altA <- extract_probabilities1(females_altA, age_classes, "Female")

final_data_tra_altA <- rbind(males_data_tra_altA, females_data_tra_altA)

graph_prob_mf_tra_altA <- ggplot(final_data_tra_altA, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, alternative scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_altA

Comparison across alternative scenarios A1/A2 (probability of remaining MPD):

extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_rem_comb_alt <- extract_probabilities_comb1(males_alt, age_classes, "Male", "Alternative A1")
females_data_rem_comb_alt <- extract_probabilities_comb1(females_alt, age_classes, "Female", "Alternative A1")

# Extract data for alternative scenario
males_data_rem_alt_comb_altA <- extract_probabilities_comb1(males_altA, age_classes, "Male", "Alternative A2")
females_data_rem_alt_comb_altA <- extract_probabilities_comb1(females_altA, age_classes, "Female", "Alternative A2")

# Combine all data
final_data_rem_combA <- rbind(males_data_rem_comb_alt, females_data_rem_comb_alt, males_data_rem_alt_comb_altA, females_data_rem_alt_comb_altA)

# Create the combined graph
graph_prob_mf_rem_combinedA <- ggplot(final_data_rem_combA, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD: comparison across alternative scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_rem_combinedA

As expected, this probability is lower than that of the previous alternative scenario (A1)

Comparison across alternative scenarios (probability of transitioning from MPD to APD):

extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_tra_comb_alt <- extract_probabilities_comb2(males_alt, age_classes, "Male", "Alternative A1")
females_data_tra_comb_alt <- extract_probabilities_comb2(females_alt, age_classes, "Female", "Alternative A1")

# Extract data for alternative scenario
males_data_tra_alt_comb_altA <- extract_probabilities_comb2(males_altA, age_classes, "Male", "Alternative A2")
females_data_tra_alt_comb_altA <- extract_probabilities_comb2(females_altA, age_classes, "Female", "Alternative A2")

# Combine all data
final_data_tra_combA <- rbind(males_data_tra_comb_alt, females_data_tra_comb_alt, males_data_tra_alt_comb_altA, females_data_tra_alt_comb_altA)

# Create the combined graph
graph_prob_mf_tra_combinedA <- ggplot(final_data_tra_combA, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD: comparison across alternative scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_tra_combinedA

Again, as expected, this probability is lower than that of alternative scenario A1, even if in a slight way. This decrease in P(MPD→APD) is assumed to counterbalance the decrease in P(MPD→MPD).

Comparison across alternative scenarios (probability of dying when MPD):

extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_dyingMPD = matrix[2, 4],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_die_comb_alt <- extract_probabilities_comb3(males_alt, age_classes, "Male", "Alternative A1")
females_data_die_comb_alt <- extract_probabilities_comb3(females_alt, age_classes, "Female", "Alternative A1")

# Extract data for alternative scenario
males_data_die_alt_comb_altA <- extract_probabilities_comb3(males_altA, age_classes, "Male", "Alternative A2")
females_data_die_alt_comb_altA <- extract_probabilities_comb3(females_altA, age_classes, "Female", "Alternative A2")

# Combine all data
final_data_die_combA <- rbind(males_data_die_comb_alt, females_data_die_comb_alt, males_data_die_alt_comb_altA, females_data_die_alt_comb_altA)

# Create the combined graph
graph_prob_mf_die_combinedA <- ggplot(final_data_die_combA, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of dying when MPD: comparison across alternative scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_die_combinedA

This time the probability is higher, which means that patients are more prone to death in this scenario. This is a positive remark since alternative scenario A2 does not consider any gain in terms of life expectancy.

Comparison wrt the baseline scenario (probability of remaining MPD):

extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_rem_comb <- extract_probabilities_comb1(males, age_classes, "Male", "Baseline")
females_data_rem_comb <- extract_probabilities_comb1(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_rem_alt_comb_altA <- extract_probabilities_comb1(males_altA, age_classes, "Male", "Alternative A2")
females_data_rem_alt_comb_altA <- extract_probabilities_comb1(females_altA, age_classes, "Female", "Alternative A2")

# Combine all data
final_data_rem_comb_A <- rbind(males_data_rem_comb, females_data_rem_comb, males_data_rem_alt_comb_altA, females_data_rem_alt_comb_altA)

# Create the combined graph
graph_prob_mf_rem_combined_A <- ggplot(final_data_rem_comb_A, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_rem_combined_A

Comparison wrt the baseline scenario (probability of transitioning from MPD to APD):

extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_tra_comb <- extract_probabilities_comb2(males, age_classes, "Male", "Baseline")
females_data_tra_comb <- extract_probabilities_comb2(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_tra_alt_comb_altA <- extract_probabilities_comb2(males_altA, age_classes, "Male", "Alternative A2")
females_data_tra_alt_comb_altA <- extract_probabilities_comb2(females_altA, age_classes, "Female", "Alternative A2")

# Combine all data
final_data_tra_comb_A <- rbind(males_data_tra_comb, females_data_tra_comb, males_data_tra_alt_comb_altA, females_data_tra_alt_comb_altA)

# Create the combined graph
graph_prob_mf_tra_combined_A <- ggplot(final_data_tra_comb_A, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_tra_combined_A

Comparison wrt the baseline scenario (probability of dying when MPD):

extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_dyingMPD = matrix[2, 4],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_die_comb <- extract_probabilities_comb3(males, age_classes, "Male", "Baseline")
females_data_die_comb <- extract_probabilities_comb3(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_die_alt_comb_altA <- extract_probabilities_comb3(males_altA, age_classes, "Male", "Alternative A2")
females_data_die_alt_comb_altA <- extract_probabilities_comb3(females_altA, age_classes, "Female", "Alternative A2")

# Combine all data
final_data_die_comb_A <- rbind(males_data_die_comb, females_data_die_comb, males_data_die_alt_comb_altA, females_data_die_alt_comb_altA)

# Create the combined graph
graph_prob_mf_die_combined_A <- ggplot(final_data_die_comb_A, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of dying when MPD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_die_combined_A

The new version of the microsimulation model is to be initialized:

n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 period

Costs in alternative scenarios are slightly different from those of the baseline scenario due to anticipation in the detection of the disease. In particular, the 1-year gain in delaying the onset of PD is associated with an early detection of 2 years (note2: why?), resulting in an early treatment of prodromal patients. All patients begin the model as prodromal in “cycle 0”, after which they either transition to MPD or pass away in “cycle 1” and this means that these patients are treated 2 years in advance before the beginning of “cycle 1”. Accordingly, the additional medical expense is equal to the 2 fifths of “c”, which is the average extra cost of a MPD patient during the 5-year cycle of the model.

#Males
transition_costs_m_alt <- list()
for (cycle in 1:10) {
  c.P.m <- costs_model_males[[cycle, "cp"]] + ((2/5)*costs_model_males[[cycle, "c"]])
  c.MPD.m <- costs_model_males[[cycle, "c"]]
  c.APD.m <- costs_model_males[[cycle, "C"]]
  c.D.m <- costs_model_males[[cycle, "D"]]
  transition_costs_m_alt[[cycle]] <- list(
    "P" = c(c.P.m),
    "MPD" = c(c.MPD.m),
    "APD" = c(c.APD.m),
    "D" = c(c.D.m)
  )
  
}

#Costs are repeated for 95+
last_transition_m_alt <- transition_costs_m_alt[[10]]
for (i in 11:n.t) {
  transition_costs_m_alt[[i]] <- last_transition_m_alt
}
print(transition_costs_m_alt)
## [[1]]
## [[1]]$P
## [1] 28260.64
## 
## [[1]]$MPD
## [1] 30039.15
## 
## [[1]]$APD
## [1] 82777.9
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 27026.7
## 
## [[2]]$MPD
## [1] 18805.09
## 
## [[2]]$APD
## [1] 52417.23
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 24032.15
## 
## [[3]]$MPD
## [1] 14841.59
## 
## [[3]]$APD
## [1] 54636.55
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 27575
## 
## [[4]]$MPD
## [1] 18675.96
## 
## [[4]]$APD
## [1] 46795.03
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 31487.79
## 
## [[5]]$MPD
## [1] 18764.37
## 
## [[5]]$APD
## [1] 45958.37
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 34797.93
## 
## [[6]]$MPD
## [1] 17788
## 
## [[6]]$APD
## [1] 36210.67
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 37455.06
## 
## [[7]]$MPD
## [1] 15104.06
## 
## [[7]]$APD
## [1] 33332.77
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 37602.5
## 
## [[8]]$MPD
## [1] 9020.232
## 
## [[8]]$APD
## [1] 23602.49
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 36466.5
## 
## [[9]]$MPD
## [1] 5341.272
## 
## [[9]]$APD
## [1] 19485.06
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 33886.03
## 
## [[10]]$MPD
## [1] 6355.477
## 
## [[10]]$APD
## [1] 0
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 33886.03
## 
## [[11]]$MPD
## [1] 6355.477
## 
## [[11]]$APD
## [1] 0
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 33886.03
## 
## [[12]]$MPD
## [1] 6355.477
## 
## [[12]]$APD
## [1] 0
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 33886.03
## 
## [[13]]$MPD
## [1] 6355.477
## 
## [[13]]$APD
## [1] 0
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 33886.03
## 
## [[14]]$MPD
## [1] 6355.477
## 
## [[14]]$APD
## [1] 0
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 33886.03
## 
## [[15]]$MPD
## [1] 6355.477
## 
## [[15]]$APD
## [1] 0
## 
## [[15]]$D
## [1] 0
#Females
transition_costs_f_alt <- list()
for (cycle in 1:10) {
  c.P.f <- costs_model_females[[cycle, "cp"]] + ((2/5)*costs_model_females[[cycle, "c"]])
  c.MPD.f <- costs_model_females[[cycle, "c"]]
  c.APD.f <- costs_model_females[[cycle, "C"]]
  c.D.f <- costs_model_females[[cycle, "D"]]
  transition_costs_f_alt[[cycle]] <- list(
    "P" = c(c.P.f),
    "MPD" = c(c.MPD.f),
    "APD" = c(c.APD.f),
    "D" = c(c.D.f)
  )
  
}

#Costs are repeated for 95+
last_transition_f_alt <- transition_costs_f_alt[[10]]
for (i in 11:n.t) {
  transition_costs_f_alt[[i]] <- last_transition_f_alt
}

print(transition_costs_f_alt)
## [[1]]
## [[1]]$P
## [1] 25124.56
## 
## [[1]]$MPD
## [1] 24292.53
## 
## [[1]]$APD
## [1] 55993.02
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 26874.58
## 
## [[2]]$MPD
## [1] 24368.35
## 
## [[2]]$APD
## [1] 66431.63
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 21895.67
## 
## [[3]]$MPD
## [1] 16594.83
## 
## [[3]]$APD
## [1] 64962.58
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 22633.31
## 
## [[4]]$MPD
## [1] 15286.68
## 
## [[4]]$APD
## [1] 50340.51
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 28864.52
## 
## [[5]]$MPD
## [1] 21780.85
## 
## [[5]]$APD
## [1] 34621.54
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 31653.34
## 
## [[6]]$MPD
## [1] 18533.03
## 
## [[6]]$APD
## [1] 41807.45
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 36832.21
## 
## [[7]]$MPD
## [1] 19459.15
## 
## [[7]]$APD
## [1] 42848.83
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 38166.8
## 
## [[8]]$MPD
## [1] 12637.32
## 
## [[8]]$APD
## [1] 34938.64
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 35370.47
## 
## [[9]]$MPD
## [1] 2801.658
## 
## [[9]]$APD
## [1] 35427.99
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 30843.99
## 
## [[10]]$MPD
## [1] 0
## 
## [[10]]$APD
## [1] 11693.52
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 30843.99
## 
## [[11]]$MPD
## [1] 0
## 
## [[11]]$APD
## [1] 11693.52
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 30843.99
## 
## [[12]]$MPD
## [1] 0
## 
## [[12]]$APD
## [1] 11693.52
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 30843.99
## 
## [[13]]$MPD
## [1] 0
## 
## [[13]]$APD
## [1] 11693.52
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 30843.99
## 
## [[14]]$MPD
## [1] 0
## 
## [[14]]$APD
## [1] 11693.52
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 30843.99
## 
## [[15]]$MPD
## [1] 0
## 
## [[15]]$APD
## [1] 11693.52
## 
## [[15]]$D
## [1] 0

The microsimulation function for male patients is:

m.M <- m.C <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_males
#Males
Probs <- function(state){
  return(transition_prob_m_alt[[state]])
}
Costs <- function(state) {
  return(transition_costs_m[[state]])
}

# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_m_altA <- transition_prob_m_altA %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altA <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M_altA[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_m_altA[[10]][[current_state]]), 1, prob = transition_prob_m_altA[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_m_altA[[t]][[current_state]]), 1, prob = transition_prob_m_altA[[t]][[current_state]])
         }
        m.M_altA[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M_altA)
}

# Init m.M #repeat it!!!!
model_results_m_altA <- list()
for(i in 1:n.sim) {
m.M_altA <- m.C_altA <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M_altA[, 1] <- v.M_1_males
# Microsim loop
model_results_m_altA[[i]] <- loop_microsim_altA(n.t)
print(i)
} 
## [1] 1
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## [1] 100
# repeat it!!!


#Results of the median simulation, the 50th
model_results_m_altA[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 2   "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 3   "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 5   "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 7   "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 8   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 9   "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 11  "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 15  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 17  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 18  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 19  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 22  "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 26  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 30  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 31  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 32  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 33  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 38  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 43  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 48  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 53  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 55  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 60  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 61  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 64  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 65  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 66  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 70  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 71  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 72  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 74  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 76  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 80  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 81  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 82  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 85  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 87  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 90  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 92  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 94  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 96  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 97  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 98  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 101 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 102 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 104 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 105 "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 107 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 108 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 112 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 116 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 117 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 120 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 124 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 125 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 128 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 129 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 132 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 133 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 135 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 137 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 139 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 141 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 143 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 145 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 152 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 155 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 156 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 164 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 165 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 166 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 168 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 173 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 174 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 178 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 179 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 182 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 188 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 190 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 196 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 197 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 198 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 203 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 204 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 206 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 207 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 208 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 209 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 211 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 213 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 214 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 217 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 220 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 224 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 228 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 230 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 233 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 235 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 238 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 239 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 241 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 243 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 244 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 250 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 255 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 258 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 259 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 267 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 268 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 269 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 270 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 272 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 273 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 278 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 279 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 281 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 287 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 294 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 296 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 299 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 2   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 263 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 264 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 265 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 266 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 267 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 268 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 269 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 270 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 271 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 272 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 273 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 274 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 275 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 276 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 277 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 278 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 279 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 280 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 281 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 282 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 283 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 284 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 285 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 286 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 287 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 288 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 289 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 290 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 291 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 292 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 293 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 294 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 295 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 296 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 297 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 298 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 299 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 300 "D"     "D"      "D"      "D"      "D"      "D"      "D"
df_m.M_altA <- model_results_m_altA[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M_altA %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 15600       1
## 
## [[2]]
##  cycle 1     n    percent
##        D   475 0.03044872
##      MPD 15125 0.96955128
## 
## [[3]]
##  cycle 2     n    percent
##      APD   981 0.06288462
##        D  1452 0.09307692
##      MPD 13167 0.84403846
## 
## [[4]]
##  cycle 3     n    percent
##      APD  1526 0.09782051
##        D  3033 0.19442308
##      MPD 11041 0.70775641
## 
## [[5]]
##  cycle 4    n   percent
##      APD 1655 0.1060897
##        D 5457 0.3498077
##      MPD 8488 0.5441026
## 
## [[6]]
##  cycle 5    n    percent
##      APD 1316 0.08435897
##        D 8184 0.52461538
##      MPD 6100 0.39102564
## 
## [[7]]
##  cycle 6     n    percent
##      APD   877 0.05621795
##        D 10768 0.69025641
##      MPD  3955 0.25352564
## 
## [[8]]
##  cycle 7     n    percent
##      APD   408 0.02615385
##        D 13040 0.83589744
##      MPD  2152 0.13794872
## 
## [[9]]
##  cycle 8     n     percent
##      APD   123 0.007884615
##        D 14581 0.934679487
##      MPD   896 0.057435897
## 
## [[10]]
##  cycle 9     n     percent
##      APD    38 0.002435897
##        D 15309 0.981346154
##      MPD   253 0.016217949
## 
## [[11]]
##  cycle 10     n      percent
##       APD     5 0.0003205128
##         D 15545 0.9964743590
##       MPD    50 0.0032051282
## 
## [[12]]
##  cycle 11     n      percent
##       APD     4 0.0002564103
##         D 15587 0.9991666667
##       MPD     9 0.0005769231
## 
## [[13]]
##  cycle 12     n      percent
##         D 15597 0.9998076923
##       MPD     3 0.0001923077
## 
## [[14]]
##  cycle 13     n       percent
##         D 15599 0.99993589744
##       MPD     1 0.00006410256
## 
## [[15]]
##  cycle 14     n       percent
##         D 15599 0.99993589744
##       MPD     1 0.00006410256
# Transition costs in a dataframe
transition_costs_m_alt <-
 transition_costs_m_alt %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
   "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")


final_cost_m_altA <-
  map(
    model_results_m_altA,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_m_alt
      )
  )
  

final_cost_m2_altA <-
  map(
    final_cost_m_altA,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
     filter(cycle != "cycle 15")
  )
final_cost_m2_altA
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 249905197.
##  4 cycle 3  15600 278777411.
##  5 cycle 4  15600 237486831.
##  6 cycle 5  15600 158886033.
##  7 cycle 6  15600  90930856.
##  8 cycle 7  15600  28647776.
##  9 cycle 8  15600   7434166.
## 10 cycle 9  15600   1696912.
## 11 cycle 10 15600    343196.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 250424000.
##  4 cycle 3  15600 278056389.
##  5 cycle 4  15600 236401785.
##  6 cycle 5  15600 157901998.
##  7 cycle 6  15600  90821494.
##  8 cycle 7  15600  30370929.
##  9 cycle 8  15600   7907149.
## 10 cycle 9  15600   1525314.
## 11 cycle 10 15600    311418.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 249453263.
##  4 cycle 3  15600 276335047.
##  5 cycle 4  15600 235315121.
##  6 cycle 5  15600 156560916.
##  7 cycle 6  15600  89136088.
##  8 cycle 7  15600  28864117.
##  9 cycle 8  15600   7413486.
## 10 cycle 9  15600   1633358.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285122707.
##  3 cycle 2  15600 248711348.
##  4 cycle 3  15600 277862289.
##  5 cycle 4  15600 236466508.
##  6 cycle 5  15600 158315547.
##  7 cycle 6  15600  90602211.
##  8 cycle 7  15600  29478387.
##  9 cycle 8  15600   7688368.
## 10 cycle 9  15600   1912999.
## 11 cycle 10 15600    394040.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 248773975.
##  4 cycle 3  15600 276058482.
##  5 cycle 4  15600 233630661.
##  6 cycle 5  15600 157486510.
##  7 cycle 6  15600  90205861.
##  8 cycle 7  15600  29524382.
##  9 cycle 8  15600   7337426.
## 10 cycle 9  15600   1633358.
## 11 cycle 10 15600    292352.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285085097.
##  3 cycle 2  15600 250626400.
##  4 cycle 3  15600 278830705.
##  5 cycle 4  15600 236631577.
##  6 cycle 5  15600 158398775.
##  7 cycle 6  15600  90716275.
##  8 cycle 7  15600  29641500.
##  9 cycle 8  15600   7444251.
## 10 cycle 9  15600   1652424.
## 11 cycle 10 15600    311418.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285799690.
##  3 cycle 2  15600 252018572.
##  4 cycle 3  15600 282239402.
##  5 cycle 4  15600 240830934.
##  6 cycle 5  15600 161185110.
##  7 cycle 6  15600  93342805.
##  8 cycle 7  15600  31572407.
##  9 cycle 8  15600   7841472.
## 10 cycle 9  15600   1544381.
## 11 cycle 10 15600    273286.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 247855430.
##  4 cycle 3  15600 276700595.
##  5 cycle 4  15600 234292843.
##  6 cycle 5  15600 158214531.
##  7 cycle 6  15600  90036592.
##  8 cycle 7  15600  28938383.
##  9 cycle 8  15600   7653843.
## 10 cycle 9  15600   1760467.
## 11 cycle 10 15600    247864.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 249003285.
##  4 cycle 3  15600 276498103.
##  5 cycle 4  15600 236695204.
##  6 cycle 5  15600 159147123.
##  7 cycle 6  15600  91103760.
##  8 cycle 7  15600  30320266.
##  9 cycle 8  15600   7547018.
## 10 cycle 9  15600   1595225.
## 11 cycle 10 15600    330485.
## 12 cycle 11 15600     50844.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285574029.
##  3 cycle 2  15600 249230639.
##  4 cycle 3  15600 276704800.
##  5 cycle 4  15600 237626038.
##  6 cycle 5  15600 160331904.
##  7 cycle 6  15600  90883977.
##  8 cycle 7  15600  30243147.
##  9 cycle 8  15600   7524669.
## 10 cycle 9  15600   1582514.
## 11 cycle 10 15600    349551.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 249981361.
##  4 cycle 3  15600 280766945.
##  5 cycle 4  15600 238317319.
##  6 cycle 5  15600 159959625.
##  7 cycle 6  15600  91125113.
##  8 cycle 7  15600  29683576.
##  9 cycle 8  15600   7409726.
## 10 cycle 9  15600   1627002.
## 11 cycle 10 15600    298707.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 248799581.
##  4 cycle 3  15600 277066773.
##  5 cycle 4  15600 235531291.
##  6 cycle 5  15600 158167531.
##  7 cycle 6  15600  90623045.
##  8 cycle 7  15600  29955393.
##  9 cycle 8  15600   7320119.
## 10 cycle 9  15600   1760467.
## 11 cycle 10 15600    438528.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284314088.
##  3 cycle 2  15600 249581291.
##  4 cycle 3  15600 279166050.
##  5 cycle 4  15600 238865295.
##  6 cycle 5  15600 158554407.
##  7 cycle 6  15600  91687098.
##  8 cycle 7  15600  29881128.
##  9 cycle 8  15600   7747508.
## 10 cycle 9  15600   1627002.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285461198.
##  3 cycle 2  15600 251387887.
##  4 cycle 3  15600 280350186.
##  5 cycle 4  15600 235730078.
##  6 cycle 5  15600 158944491.
##  7 cycle 6  15600  91247522.
##  8 cycle 7  15600  28828181.
##  9 cycle 8  15600   7339007.
## 10 cycle 9  15600   1601580.
## 11 cycle 10 15600    311418.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 250480429.
##  4 cycle 3  15600 278247985.
##  5 cycle 4  15600 238313795.
##  6 cycle 5  15600 157580527.
##  7 cycle 6  15600  90151694.
##  8 cycle 7  15600  29058643.
##  9 cycle 8  15600   7111212.
## 10 cycle 9  15600   1582514.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 249664306.
##  4 cycle 3  15600 278095002.
##  5 cycle 4  15600 236004733.
##  6 cycle 5  15600 156578704.
##  7 cycle 6  15600  89066817.
##  8 cycle 7  15600  28908613.
##  9 cycle 8  15600   7939495.
## 10 cycle 9  15600   1715979.
## 11 cycle 10 15600    381329.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 248709227.
##  4 cycle 3  15600 275159723.
##  5 cycle 4  15600 234466628.
##  6 cycle 5  15600 157003678.
##  7 cycle 6  15600  88208491.
##  8 cycle 7  15600  28925444.
##  9 cycle 8  15600   6940204.
## 10 cycle 9  15600   1633358.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 251708203.
##  4 cycle 3  15600 278293728.
##  5 cycle 4  15600 237089256.
##  6 cycle 5  15600 159932317.
##  7 cycle 6  15600  90751165.
##  8 cycle 7  15600  29368040.
##  9 cycle 8  15600   7711614.
## 10 cycle 9  15600   1722334.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 252430059.
##  4 cycle 3  15600 282126085.
##  5 cycle 4  15600 240276720.
##  6 cycle 5  15600 159271639.
##  7 cycle 6  15600  90499617.
##  8 cycle 7  15600  29826862.
##  9 cycle 8  15600   7503603.
## 10 cycle 9  15600   1696912.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284803020.
##  3 cycle 2  15600 249585533.
##  4 cycle 3  15600 276889457.
##  5 cycle 4  15600 235281639.
##  6 cycle 5  15600 156125136.
##  7 cycle 6  15600  89098083.
##  8 cycle 7  15600  30482025.
##  9 cycle 8  15600   8041963.
## 10 cycle 9  15600   1658779.
## 11 cycle 10 15600    324129.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284276478.
##  3 cycle 2  15600 248379939.
##  4 cycle 3  15600 275030043.
##  5 cycle 4  15600 235410798.
##  6 cycle 5  15600 158677002.
##  7 cycle 6  15600  92373011.
##  8 cycle 7  15600  29333602.
##  9 cycle 8  15600   7454336.
## 10 cycle 9  15600   1493537.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 247512604.
##  4 cycle 3  15600 277705120.
##  5 cycle 4  15600 237741912.
##  6 cycle 5  15600 160047964.
##  7 cycle 6  15600  91615750.
##  8 cycle 7  15600  29694123.
##  9 cycle 8  15600   7254144.
## 10 cycle 9  15600   1690557.
## 11 cycle 10 15600    362262.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 249974675.
##  4 cycle 3  15600 278479647.
##  5 cycle 4  15600 239011364.
##  6 cycle 5  15600 158579194.
##  7 cycle 6  15600  90328261.
##  8 cycle 7  15600  29986951.
##  9 cycle 8  15600   7516252.
## 10 cycle 9  15600   1760467.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     38133.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284746605.
##  3 cycle 2  15600 251812582.
##  4 cycle 3  15600 277330760.
##  5 cycle 4  15600 237559983.
##  6 cycle 5  15600 160281747.
##  7 cycle 6  15600  91008463.
##  8 cycle 7  15600  29620895.
##  9 cycle 8  15600   7247520.
## 10 cycle 9  15600   1722334.
## 11 cycle 10 15600    305063.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 250270038.
##  4 cycle 3  15600 278151871.
##  5 cycle 4  15600 237512120.
##  6 cycle 5  15600 159011818.
##  7 cycle 6  15600  90738147.
##  8 cycle 7  15600  29725680.
##  9 cycle 8  15600   7410323.
## 10 cycle 9  15600   1518959.
## 11 cycle 10 15600    336840.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 247234692.
##  4 cycle 3  15600 276629676.
##  5 cycle 4  15600 235465186.
##  6 cycle 5  15600 160108928.
##  7 cycle 6  15600  93307386.
##  8 cycle 7  15600  29859629.
##  9 cycle 8  15600   7537618.
## 10 cycle 9  15600   1690557.
## 11 cycle 10 15600    305063.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 250571599.
##  4 cycle 3  15600 279967855.
##  5 cycle 4  15600 236797169.
##  6 cycle 5  15600 158678923.
##  7 cycle 6  15600  88917343.
##  8 cycle 7  15600  28718583.
##  9 cycle 8  15600   7729306.
## 10 cycle 9  15600   1576158.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284878241.
##  3 cycle 2  15600 250136628.
##  4 cycle 3  15600 278118304.
##  5 cycle 4  15600 234010568.
##  6 cycle 5  15600 156531052.
##  7 cycle 6  15600  89454831.
##  8 cycle 7  15600  29427868.
##  9 cycle 8  15600   7234659.
## 10 cycle 9  15600   1442693.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 247576865.
##  4 cycle 3  15600 277741211.
##  5 cycle 4  15600 235554388.
##  6 cycle 5  15600 159367578.
##  7 cycle 6  15600  91628239.
##  8 cycle 7  15600  30998426.
##  9 cycle 8  15600   7721997.
## 10 cycle 9  15600   1747756.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 252523509.
##  4 cycle 3  15600 281281270.
##  5 cycle 4  15600 236760686.
##  6 cycle 5  15600 156374735.
##  7 cycle 6  15600  87758493.
##  8 cycle 7  15600  28457602.
##  9 cycle 8  15600   7251965.
## 10 cycle 9  15600   1588869.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 248965611.
##  4 cycle 3  15600 277369584.
##  5 cycle 4  15600 235096136.
##  6 cycle 5  15600 156510709.
##  7 cycle 6  15600  89270457.
##  8 cycle 7  15600  28667171.
##  9 cycle 8  15600   7230600.
## 10 cycle 9  15600   1671490.
## 11 cycle 10 15600    343196.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 250142010.
##  4 cycle 3  15600 278124593.
##  5 cycle 4  15600 236126035.
##  6 cycle 5  15600 158550599.
##  7 cycle 6  15600  90840751.
##  8 cycle 7  15600  30114299.
##  9 cycle 8  15600   7596670.
## 10 cycle 9  15600   1817666.
## 11 cycle 10 15600    387684.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 247930778.
##  4 cycle 3  15600 276404513.
##  5 cycle 4  15600 235426848.
##  6 cycle 5  15600 157198712.
##  7 cycle 6  15600  89086084.
##  8 cycle 7  15600  28988441.
##  9 cycle 8  15600   7667987.
## 10 cycle 9  15600   1576158.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 248765168.
##  4 cycle 3  15600 277696098.
##  5 cycle 4  15600 235831234.
##  6 cycle 5  15600 157158710.
##  7 cycle 6  15600  89213685.
##  8 cycle 7  15600  29103744.
##  9 cycle 8  15600   7829209.
## 10 cycle 9  15600   1735045.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 248149488.
##  4 cycle 3  15600 277932577.
##  5 cycle 4  15600 236957382.
##  6 cycle 5  15600 159150948.
##  7 cycle 6  15600  90304831.
##  8 cycle 7  15600  30494503.
##  9 cycle 8  15600   8043544.
## 10 cycle 9  15600   1646068.
## 11 cycle 10 15600    394040.
## 12 cycle 11 15600    114399.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 249031664.
##  4 cycle 3  15600 278828832.
##  5 cycle 4  15600 237872689.
##  6 cycle 5  15600 156996062.
##  7 cycle 6  15600  90323040.
##  8 cycle 7  15600  29460635.
##  9 cycle 8  15600   7795281.
## 10 cycle 9  15600   1658779.
## 11 cycle 10 15600    285996.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 251512489.
##  4 cycle 3  15600 279733880.
##  5 cycle 4  15600 236088220.
##  6 cycle 5  15600 157452203.
##  7 cycle 6  15600  89443380.
##  8 cycle 7  15600  28351318.
##  9 cycle 8  15600   7006864.
## 10 cycle 9  15600   1779533.
## 11 cycle 10 15600    381329.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 248912279.
##  4 cycle 3  15600 278068566.
##  5 cycle 4  15600 237237466.
##  6 cycle 5  15600 158454026.
##  7 cycle 6  15600  91099587.
##  8 cycle 7  15600  30106028.
##  9 cycle 8  15600   7641878.
## 10 cycle 9  15600   1741401.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285555224.
##  3 cycle 2  15600 250135323.
##  4 cycle 3  15600 280677962.
##  5 cycle 4  15600 238034521.
##  6 cycle 5  15600 158865672.
##  7 cycle 6  15600  90158453.
##  8 cycle 7  15600  28032007.
##  9 cycle 8  15600   7032587.
## 10 cycle 9  15600   1525314.
## 11 cycle 10 15600    285996.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284238868.
##  3 cycle 2  15600 249331105.
##  4 cycle 3  15600 279033637.
##  5 cycle 4  15600 238898205.
##  6 cycle 5  15600 157645950.
##  7 cycle 6  15600  89320452.
##  8 cycle 7  15600  28798411.
##  9 cycle 8  15600   7227438.
## 10 cycle 9  15600   1639713.
## 11 cycle 10 15600    330485.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 247590726.
##  4 cycle 3  15600 276089755.
##  5 cycle 4  15600 236367207.
##  6 cycle 5  15600 159401216.
##  7 cycle 6  15600  89720964.
##  8 cycle 7  15600  29319165.
##  9 cycle 8  15600   7409427.
## 10 cycle 9  15600   1601580.
## 11 cycle 10 15600    387684.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 250760462.
##  4 cycle 3  15600 279358468.
##  5 cycle 4  15600 238316796.
##  6 cycle 5  15600 158243109.
##  7 cycle 6  15600  89223068.
##  8 cycle 7  15600  28571406.
##  9 cycle 8  15600   7613292.
## 10 cycle 9  15600   1741401.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 250702565.
##  4 cycle 3  15600 279808584.
##  5 cycle 4  15600 239669162.
##  6 cycle 5  15600 158679558.
##  7 cycle 6  15600  91239706.
##  8 cycle 7  15600  29627523.
##  9 cycle 8  15600   7518431.
## 10 cycle 9  15600   1735045.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 249090378.
##  4 cycle 3  15600 276497051.
##  5 cycle 4  15600 234593595.
##  6 cycle 5  15600 157647219.
##  7 cycle 6  15600  89588162.
##  8 cycle 7  15600  28295842.
##  9 cycle 8  15600   7569964.
## 10 cycle 9  15600   1525314.
## 11 cycle 10 15600    266930.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 250206594.
##  4 cycle 3  15600 277923994.
##  5 cycle 4  15600 232964906.
##  6 cycle 5  15600 157340364.
##  7 cycle 6  15600  89347535.
##  8 cycle 7  15600  28999104.
##  9 cycle 8  15600   7607353.
## 10 cycle 9  15600   1836733.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 248077725.
##  4 cycle 3  15600 274506695.
##  5 cycle 4  15600 234652889.
##  6 cycle 5  15600 156471959.
##  7 cycle 6  15600  88263186.
##  8 cycle 7  15600  29173803.
##  9 cycle 8  15600   7336442.
## 10 cycle 9  15600   1766823.
## 11 cycle 10 15600    336840.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 252218035.
##  4 cycle 3  15600 280187360.
##  5 cycle 4  15600 236199710.
##  6 cycle 5  15600 160137505.
##  7 cycle 6  15600  90539699.
##  8 cycle 7  15600  29186570.
##  9 cycle 8  15600   7737809.
## 10 cycle 9  15600   1557092.
## 11 cycle 10 15600    324129.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 248290401.
##  4 cycle 3  15600 276928701.
##  5 cycle 4  15600 234244457.
##  6 cycle 5  15600 155642321.
##  7 cycle 6  15600  88602244.
##  8 cycle 7  15600  29508301.
##  9 cycle 8  15600   7697856.
## 10 cycle 9  15600   1607936.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 252107457.
##  4 cycle 3  15600 280260802.
##  5 cycle 4  15600 235463280.
##  6 cycle 5  15600 156760358.
##  7 cycle 6  15600  88982951.
##  8 cycle 7  15600  29473718.
##  9 cycle 8  15600   7298456.
## 10 cycle 9  15600   1595225.
## 11 cycle 10 15600    247864.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 249017639.
##  4 cycle 3  15600 277610479.
##  5 cycle 4  15600 235333026.
##  6 cycle 5  15600 156160026.
##  7 cycle 6  15600  88969414.
##  8 cycle 7  15600  29041352.
##  9 cycle 8  15600   7182442.
## 10 cycle 9  15600   1607936.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285216732.
##  3 cycle 2  15600 249346111.
##  4 cycle 3  15600 279040556.
##  5 cycle 4  15600 235222631.
##  6 cycle 5  15600 158567118.
##  7 cycle 6  15600  89741808.
##  8 cycle 7  15600  30583640.
##  9 cycle 8  15600   7666406.
## 10 cycle 9  15600   1658779.
## 11 cycle 10 15600    387684.
## 12 cycle 11 15600    127110.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 248252563.
##  4 cycle 3  15600 277180511.
##  5 cycle 4  15600 236757735.
##  6 cycle 5  15600 159795725.
##  7 cycle 6  15600  91424589.
##  8 cycle 7  15600  29215445.
##  9 cycle 8  15600   7984790.
## 10 cycle 9  15600   1544381.
## 11 cycle 10 15600    305063.
## 12 cycle 11 15600     38133.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 248210811.
##  4 cycle 3  15600 277683501.
##  5 cycle 4  15600 237083018.
##  6 cycle 5  15600 158494714.
##  7 cycle 6  15600  90423058.
##  8 cycle 7  15600  30050119.
##  9 cycle 8  15600   7337339.
## 10 cycle 9  15600   1709623.
## 11 cycle 10 15600    432172.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284897046.
##  3 cycle 2  15600 249001981.
##  4 cycle 3  15600 275390754.
##  5 cycle 4  15600 235393416.
##  6 cycle 5  15600 157743793.
##  7 cycle 6  15600  89311087.
##  8 cycle 7  15600  29794239.
##  9 cycle 8  15600   7274227.
## 10 cycle 9  15600   1601580.
## 11 cycle 10 15600    413106.
## 12 cycle 11 15600    139820.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 249128704.
##  4 cycle 3  15600 277106438.
##  5 cycle 4  15600 234189782.
##  6 cycle 5  15600 157502377.
##  7 cycle 6  15600  91430329.
##  8 cycle 7  15600  30588308.
##  9 cycle 8  15600   7779556.
## 10 cycle 9  15600   1633358.
## 11 cycle 10 15600    298707.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285592834.
##  3 cycle 2  15600 251886954.
##  4 cycle 3  15600 280704189.
##  5 cycle 4  15600 239660210.
##  6 cycle 5  15600 161009769.
##  7 cycle 6  15600  92462606.
##  8 cycle 7  15600  29690348.
##  9 cycle 8  15600   7542573.
## 10 cycle 9  15600   1595225.
## 11 cycle 10 15600    298707.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 250074488.
##  4 cycle 3  15600 279939526.
##  5 cycle 4  15600 236462125.
##  6 cycle 5  15600 159910704.
##  7 cycle 6  15600  91708442.
##  8 cycle 7  15600  29139509.
##  9 cycle 8  15600   7607739.
## 10 cycle 9  15600   1544381.
## 11 cycle 10 15600    298707.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285310758.
##  3 cycle 2  15600 249507247.
##  4 cycle 3  15600 278323109.
##  5 cycle 4  15600 238221592.
##  6 cycle 5  15600 160789314.
##  7 cycle 6  15600  91577716.
##  8 cycle 7  15600  29116828.
##  9 cycle 8  15600   7446131.
## 10 cycle 9  15600   1754112.
## 11 cycle 10 15600    349551.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 249575909.
##  4 cycle 3  15600 277810256.
##  5 cycle 4  15600 233425585.
##  6 cycle 5  15600 157017641.
##  7 cycle 6  15600  88651181.
##  8 cycle 7  15600  28087628.
##  9 cycle 8  15600   7513688.
## 10 cycle 9  15600   1646068.
## 11 cycle 10 15600    298707.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 249318220.
##  4 cycle 3  15600 275853677.
##  5 cycle 4  15600 232504799.
##  6 cycle 5  15600 153885718.
##  7 cycle 6  15600  87650159.
##  8 cycle 7  15600  29096973.
##  9 cycle 8  15600   7743449.
## 10 cycle 9  15600   1690557.
## 11 cycle 10 15600    349551.
## 12 cycle 11 15600    101688.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600      6355.
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 246803146.
##  4 cycle 3  15600 277599564.
##  5 cycle 4  15600 236780883.
##  6 cycle 5  15600 157746983.
##  7 cycle 6  15600  90770961.
##  8 cycle 7  15600  29529944.
##  9 cycle 8  15600   7928215.
## 10 cycle 9  15600   1715979.
## 11 cycle 10 15600    305063.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 250649885.
##  4 cycle 3  15600 279275373.
##  5 cycle 4  15600 234401619.
##  6 cycle 5  15600 155832226.
##  7 cycle 6  15600  88964732.
##  8 cycle 7  15600  28653338.
##  9 cycle 8  15600   7558684.
## 10 cycle 9  15600   1722334.
## 11 cycle 10 15600    317774.
## 12 cycle 11 15600     50844.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 251346623.
##  4 cycle 3  15600 279125544.
##  5 cycle 4  15600 235473902.
##  6 cycle 5  15600 155197604.
##  7 cycle 6  15600  89091814.
##  8 cycle 7  15600  29217549.
##  9 cycle 8  15600   7408145.
## 10 cycle 9  15600   1442693.
## 11 cycle 10 15600    266930.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 250815751.
##  4 cycle 3  15600 278959353.
##  5 cycle 4  15600 234009759.
##  6 cycle 5  15600 156459232.
##  7 cycle 6  15600  88164736.
##  8 cycle 7  15600  28283364.
##  9 cycle 8  15600   6976012.
## 10 cycle 9  15600   1696912.
## 11 cycle 10 15600    343196.
## 12 cycle 11 15600    146176.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600      6355.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 248644643.
##  4 cycle 3  15600 277673006.
##  5 cycle 4  15600 239954202.
##  6 cycle 5  15600 161980475.
##  7 cycle 6  15600  93295407.
##  8 cycle 7  15600  30641680.
##  9 cycle 8  15600   7973423.
## 10 cycle 9  15600   1633358.
## 11 cycle 10 15600    425817.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 250288305.
##  4 cycle 3  15600 279434644.
##  5 cycle 4  15600 235310738.
##  6 cycle 5  15600 157710138.
##  7 cycle 6  15600  88890789.
##  8 cycle 7  15600  30057930.
##  9 cycle 8  15600   7581543.
## 10 cycle 9  15600   1620647.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 248284367.
##  4 cycle 3  15600 277741421.
##  5 cycle 4  15600 237371817.
##  6 cycle 5  15600 160245519.
##  7 cycle 6  15600  90896995.
##  8 cycle 7  15600  29162534.
##  9 cycle 8  15600   7388274.
## 10 cycle 9  15600   1709623.
## 11 cycle 10 15600    336840.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 248505522.
##  4 cycle 3  15600 277217863.
##  5 cycle 4  15600 237453112.
##  6 cycle 5  15600 159403137.
##  7 cycle 6  15600  91386574.
##  8 cycle 7  15600  29291671.
##  9 cycle 8  15600   7699437.
## 10 cycle 9  15600   1531670.
## 11 cycle 10 15600    349551.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 252806152.
##  4 cycle 3  15600 279101210.
##  5 cycle 4  15600 236396879.
##  6 cycle 5  15600 158744346.
##  7 cycle 6  15600  89837114.
##  8 cycle 7  15600  29779196.
##  9 cycle 8  15600   6966911.
## 10 cycle 9  15600   1569803.
## 11 cycle 10 15600    305063.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284972266.
##  3 cycle 2  15600 248048206.
##  4 cycle 3  15600 276775509.
##  5 cycle 4  15600 233493359.
##  6 cycle 5  15600 158368294.
##  7 cycle 6  15600  90526700.
##  8 cycle 7  15600  30380699.
##  9 cycle 8  15600   7421902.
## 10 cycle 9  15600   1671490.
## 11 cycle 10 15600    330485.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 249559763.
##  4 cycle 3  15600 275560100.
##  5 cycle 4  15600 232794981.
##  6 cycle 5  15600 156685415.
##  7 cycle 6  15600  89142847.
##  8 cycle 7  15600  28498495.
##  9 cycle 8  15600   7235854.
## 10 cycle 9  15600   1696912.
## 11 cycle 10 15600    406751.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 251772787.
##  4 cycle 3  15600 280423437.
##  5 cycle 4  15600 239127423.
##  6 cycle 5  15600 159657212.
##  7 cycle 6  15600  89140760.
##  8 cycle 7  15600  29368329.
##  9 cycle 8  15600   7358791.
## 10 cycle 9  15600   1633358.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 248026026.
##  4 cycle 3  15600 276673947.
##  5 cycle 4  15600 235898148.
##  6 cycle 5  15600 156588876.
##  7 cycle 6  15600  90468889.
##  8 cycle 7  15600  29547090.
##  9 cycle 8  15600   7298754.
## 10 cycle 9  15600   1538025.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 250542081.
##  4 cycle 3  15600 278788957.
##  5 cycle 4  15600 236414548.
##  6 cycle 5  15600 157835271.
##  7 cycle 6  15600  91719902.
##  8 cycle 7  15600  30026201.
##  9 cycle 8  15600   7698453.
## 10 cycle 9  15600   1550736.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285179122.
##  3 cycle 2  15600 250317336.
##  4 cycle 3  15600 278714463.
##  5 cycle 4  15600 236680773.
##  6 cycle 5  15600 158557581.
##  7 cycle 6  15600  90944912.
##  8 cycle 7  15600  28826682.
##  9 cycle 8  15600   7533472.
## 10 cycle 9  15600   1652424.
## 11 cycle 10 15600    368618.
## 12 cycle 11 15600    108043.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 252641590.
##  4 cycle 3  15600 278812679.
##  5 cycle 4  15600 233573272.
##  6 cycle 5  15600 158605233.
##  7 cycle 6  15600  90185037.
##  8 cycle 7  15600  30075221.
##  9 cycle 8  15600   7765325.
## 10 cycle 9  15600   1868510.
## 11 cycle 10 15600    451239.
## 12 cycle 11 15600     88977.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 247997483.
##  4 cycle 3  15600 277070558.
##  5 cycle 4  15600 231763227.
##  6 cycle 5  15600 156779433.
##  7 cycle 6  15600  89216290.
##  8 cycle 7  15600  29387579.
##  9 cycle 8  15600   7333280.
## 10 cycle 9  15600   1614291.
## 11 cycle 10 15600    324129.
## 12 cycle 11 15600     50844.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285141512.
##  3 cycle 2  15600 249655011.
##  4 cycle 3  15600 279844253.
##  5 cycle 4  15600 236773262.
##  6 cycle 5  15600 158429908.
##  7 cycle 6  15600  90488156.
##  8 cycle 7  15600  29462450.
##  9 cycle 8  15600   7796476.
## 10 cycle 9  15600   1728690.
## 11 cycle 10 15600    394040.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 250280802.
##  4 cycle 3  15600 277950010.
##  5 cycle 4  15600 235830138.
##  6 cycle 5  15600 159905643.
##  7 cycle 6  15600  91490745.
##  8 cycle 7  15600  29486946.
##  9 cycle 8  15600   7376695.
## 10 cycle 9  15600   1569803.
## 11 cycle 10 15600    336840.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 283449054.
##  3 cycle 2  15600 250684950.
##  4 cycle 3  15600 278976347.
##  5 cycle 4  15600 237083540.
##  6 cycle 5  15600 158637001.
##  7 cycle 6  15600  91674599.
##  8 cycle 7  15600  30310785.
##  9 cycle 8  15600   7713581.
## 10 cycle 9  15600   1595225.
## 11 cycle 10 15600    330485.
## 12 cycle 11 15600     25422.
## 13 cycle 12 15600         0 
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 251074092.
##  4 cycle 3  15600 277247664.
##  5 cycle 4  15600 234433432.
##  6 cycle 5  15600 155916723.
##  7 cycle 6  15600  88161611.
##  8 cycle 7  15600  28149704.
##  9 cycle 8  15600   6813894.
## 10 cycle 9  15600   1563447.
## 11 cycle 10 15600    336840.
## 12 cycle 11 15600     57199.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 248455615.
##  4 cycle 3  15600 278883808.
##  5 cycle 4  15600 237726958.
##  6 cycle 5  15600 159097567.
##  7 cycle 6  15600  90517326.
##  8 cycle 7  15600  29288817.
##  9 cycle 8  15600   7430020.
## 10 cycle 9  15600   1690557.
## 11 cycle 10 15600    311418.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284332893.
##  3 cycle 2  15600 250187675.
##  4 cycle 3  15600 278961876.
##  5 cycle 4  15600 234715420.
##  6 cycle 5  15600 156148602.
##  7 cycle 6  15600  89424103.
##  8 cycle 7  15600  29395850.
##  9 cycle 8  15600   7276107.
## 10 cycle 9  15600   1506248.
## 11 cycle 10 15600    228797.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285385978.
##  3 cycle 2  15600 249025630.
##  4 cycle 3  15600 278639128.
##  5 cycle 4  15600 235797752.
##  6 cycle 5  15600 156540556.
##  7 cycle 6  15600  89960024.
##  8 cycle 7  15600  28527055.
##  9 cycle 8  15600   6966911.
## 10 cycle 9  15600   1569803.
## 11 cycle 10 15600    254219.
## 12 cycle 11 15600     19066.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 250084600.
##  4 cycle 3  15600 280220716.
##  5 cycle 4  15600 237697858.
##  6 cycle 5  15600 156778197.
##  7 cycle 6  15600  88855889.
##  8 cycle 7  15600  28598178.
##  9 cycle 8  15600   7543170.
## 10 cycle 9  15600   1671490.
## 11 cycle 10 15600    438528.
## 12 cycle 11 15600     31777.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 249296692.
##  4 cycle 3  15600 276068346.
##  5 cycle 4  15600 234957216.
##  6 cycle 5  15600 157719023.
##  7 cycle 6  15600  90165760.
##  8 cycle 7  15600  28992648.
##  9 cycle 8  15600   7552359.
## 10 cycle 9  15600   1601580.
## 11 cycle 10 15600    343196.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 249551607.
##  4 cycle 3  15600 277763671.
##  5 cycle 4  15600 235779796.
##  6 cycle 5  15600 158448915.
##  7 cycle 6  15600  90621997.
##  8 cycle 7  15600  29814067.
##  9 cycle 8  15600   7394001.
## 10 cycle 9  15600   1658779.
## 11 cycle 10 15600    419461.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 248670084.
##  4 cycle 3  15600 275923755.
##  5 cycle 4  15600 236104270.
##  6 cycle 5  15600 156421134.
##  7 cycle 6  15600  88961608.
##  8 cycle 7  15600  29173053.
##  9 cycle 8  15600   7298456.
## 10 cycle 9  15600   1442693.
## 11 cycle 10 15600    311418.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285028682.
##  3 cycle 2  15600 248469969.
##  4 cycle 3  15600 276653169.
##  5 cycle 4  15600 233303811.
##  6 cycle 5  15600 156391922.
##  7 cycle 6  15600  88610570.
##  8 cycle 7  15600  29046165.
##  9 cycle 8  15600   7044552.
## 10 cycle 9  15600   1518959.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     95332.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600     12711.
## 15 cycle 14 15600         0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 248778217.
##  4 cycle 3  15600 275322340.
##  5 cycle 4  15600 236350685.
##  6 cycle 5  15600 159557534.
##  7 cycle 6  15600  92630847.
##  8 cycle 7  15600  30549374.
##  9 cycle 8  15600   7852753.
## 10 cycle 9  15600   1601580.
## 11 cycle 10 15600    330485.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 252528404.
##  4 cycle 3  15600 280125444.
##  5 cycle 4  15600 238921066.
##  6 cycle 5  15600 159446981.
##  7 cycle 6  15600  89466820.
##  8 cycle 7  15600  28819160.
##  9 cycle 8  15600   7248504.
## 10 cycle 9  15600   1538025.
## 11 cycle 10 15600    330485.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 249861976.
##  4 cycle 3  15600 276898479.
##  5 cycle 4  15600 236922568.
##  6 cycle 5  15600 160007311.
##  7 cycle 6  15600  92170428.
##  8 cycle 7  15600  30570268.
##  9 cycle 8  15600   7709821.
## 10 cycle 9  15600   1576158.
## 11 cycle 10 15600    266930.
## 12 cycle 11 15600     44488.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 249105872.
##  4 cycle 3  15600 276672265.
##  5 cycle 4  15600 238511537.
##  6 cycle 5  15600 160420878.
##  7 cycle 6  15600  91343877.
##  8 cycle 7  15600  30817877.
##  9 cycle 8  15600   7974021.
## 10 cycle 9  15600   1836733.
## 11 cycle 10 15600    425817.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 251158412.
##  4 cycle 3  15600 280297523.
##  5 cycle 4  15600 236785452.
##  6 cycle 5  15600 158134460.
##  7 cycle 6  15600  89381377.
##  8 cycle 7  15600  28990834.
##  9 cycle 8  15600   7250085.
## 10 cycle 9  15600   1658779.
## 11 cycle 10 15600    324129.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 250469665.
##  4 cycle 3  15600 279409258.
##  5 cycle 4  15600 236543184.
##  6 cycle 5  15600 160288729.
##  7 cycle 6  15600  90368372.
##  8 cycle 7  15600  29699685.
##  9 cycle 8  15600   7409639.
## 10 cycle 9  15600   1785889.
## 11 cycle 10 15600    343196.
## 12 cycle 11 15600     76266.
## 13 cycle 12 15600     12711.
## 14 cycle 13 15600      6355.
## 15 cycle 14 15600         0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284389309.
##  3 cycle 2  15600 248270830.
##  4 cycle 3  15600 275579406.
##  5 cycle 4  15600 236226618.
##  6 cycle 5  15600 158074766.
##  7 cycle 6  15600  90908984.
##  8 cycle 7  15600  29620751.
##  9 cycle 8  15600   7693797.
## 10 cycle 9  15600   1607936.
## 11 cycle 10 15600    349551.
## 12 cycle 11 15600    120754.
## 13 cycle 12 15600     31777.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 250881804.
##  4 cycle 3  15600 279953174.
##  5 cycle 4  15600 236040879.
##  6 cycle 5  15600 157401344.
##  7 cycle 6  15600  90789699.
##  8 cycle 7  15600  30254704.
##  9 cycle 8  15600   7408356.
## 10 cycle 9  15600   1747756.
## 11 cycle 10 15600    330485.
## 12 cycle 11 15600     63555.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 248490028.
##  4 cycle 3  15600 274989957.
##  5 cycle 4  15600 235046132.
##  6 cycle 5  15600 157788271.
##  7 cycle 6  15600  91134506.
##  8 cycle 7  15600  30702718.
##  9 cycle 8  15600   8106656.
## 10 cycle 9  15600   1766823.
## 11 cycle 10 15600    343196.
## 12 cycle 11 15600     69910.
## 13 cycle 12 15600     19066.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 250139237.
##  4 cycle 3  15600 280051581.
##  5 cycle 4  15600 237283947.
##  6 cycle 5  15600 156538034.
##  7 cycle 6  15600  89147019.
##  8 cycle 7  15600  28483308.
##  9 cycle 8  15600   7229019.
## 10 cycle 9  15600   1525314.
## 11 cycle 10 15600    254219.
## 12 cycle 11 15600     50844.
## 13 cycle 12 15600     25422.
## 14 cycle 13 15600     19066.
## 15 cycle 14 15600         0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285348368.
##  3 cycle 2  15600 251132154.
##  4 cycle 3  15600 280696007.
##  5 cycle 4  15600 238825052.
##  6 cycle 5  15600 159443790.
##  7 cycle 6  15600  90394926.
##  8 cycle 7  15600  29121641.
##  9 cycle 8  15600   7946418.
## 10 cycle 9  15600   1607936.
## 11 cycle 10 15600    355907.
## 12 cycle 11 15600     82621.
## 13 cycle 12 15600      6355.
## 14 cycle 13 15600         0 
## 15 cycle 14 15600         0
m.M <- m.C <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_females

The same reasoning is applied to female patients:

#Females
Probs <- function(state){
  return(transition_prob_f_alt[[state]])
}
Costs <- function(state) {
  return(transition_costs_f[[state]])
}
# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_f_altA <- transition_prob_f_altA %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altA <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M_altA[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_f_altA[[10]][[current_state]]), 1, prob = transition_prob_f_altA[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_f_altA[[t]][[current_state]]), 1, prob = transition_prob_f_altA[[t]][[current_state]])
         }
        m.M_altA[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M_altA)
}

# Init m.M #repeat it!!!!
model_results_f_altA <- list()
for(i in 1:n.sim) {
m.M_altA <- m.C_altA <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M_altA[, 1] <- v.M_1_females
# Microsim loop
model_results_f_altA[[i]] <- loop_microsim_altA(n.t)
print(i)
}  
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
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## [1] 93
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## [1] 95
## [1] 96
## [1] 97
## [1] 98
## [1] 99
## [1] 100
# repeat it!!!

#Results of the median simulation, the 50th
model_results_f_altA[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 2   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 3   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 5   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 7   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 8   "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 9   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 11  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 15  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 17  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 18  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 19  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 22  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 26  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 30  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 31  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 32  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 33  "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 38  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 43  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 48  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 53  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 55  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 60  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 61  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 64  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 65  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 66  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 70  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 71  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 72  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 74  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 76  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 80  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 81  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 82  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 85  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 87  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 90  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 92  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 94  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 96  "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 97  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 98  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 101 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 102 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 104 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 105 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"  
## ind 107 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"  
## ind 108 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 112 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 116 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 117 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 120 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 124 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 125 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 128 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 129 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 132 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 133 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 135 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 137 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 139 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 141 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 143 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 145 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"  
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 152 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 155 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 156 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 164 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 165 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 166 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 168 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 173 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 174 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 178 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 179 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 182 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 188 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 190 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 196 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 197 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 198 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 203 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 204 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 206 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 207 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 208 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 209 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 211 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 213 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 214 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 217 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 220 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 224 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 228 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 230 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 233 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 235 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 238 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 239 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 241 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 243 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 244 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 250 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 255 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 258 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 259 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 267 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 268 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 269 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 270 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 272 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 273 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 278 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 279 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 281 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 287 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 294 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 296 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 299 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 2   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 20  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 21  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 22  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 23  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 24  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 25  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 26  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 27  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 28  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 29  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 30  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 31  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 32  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 33  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 34  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 35  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 36  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 37  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 38  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 39  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 40  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 41  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 42  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 43  "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 44  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 45  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 46  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 47  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 48  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 49  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 50  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 51  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 52  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 53  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 54  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 55  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 56  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 57  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 58  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 59  "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "MPD"    "D"     
## ind 60  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 61  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 62  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 63  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 64  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 65  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 66  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 67  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 68  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 69  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 70  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 71  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 72  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 73  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 74  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 75  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 76  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 77  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 78  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 79  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 80  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 81  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 82  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 83  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 84  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 85  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 86  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 87  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 88  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 89  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 90  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 91  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 92  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 93  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 94  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 95  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 96  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 97  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 98  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 99  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 100 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 101 "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 102 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 103 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 104 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 105 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 106 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 107 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 108 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 109 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 110 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 111 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 112 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 113 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 114 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 115 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 116 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 117 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 118 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 119 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 120 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 121 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 122 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 123 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 124 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 125 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 126 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 127 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 128 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 129 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 130 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 131 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 132 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 133 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 134 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 135 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 136 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 137 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 138 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 139 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 140 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 141 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 142 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 143 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 144 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 145 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 146 "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 147 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 148 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 149 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 150 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 151 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 152 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 153 "APD"   "APD"    "D"      "D"      "D"      "D"      "D"     
## ind 154 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 155 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 156 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 157 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 158 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 159 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 160 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 161 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 162 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 163 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 164 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 165 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 166 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 167 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 168 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 169 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 170 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 171 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 172 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 173 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 174 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 175 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 176 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 177 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 178 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 179 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 180 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 181 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 182 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 183 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 184 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 185 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 186 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 187 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 188 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 189 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 190 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 191 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 192 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 193 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 194 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 195 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 196 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 197 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 198 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 199 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 200 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 201 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 202 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 203 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 204 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 205 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 206 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 207 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 208 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 209 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 210 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 211 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 212 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 213 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 214 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 215 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 216 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 217 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 218 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 219 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 220 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 221 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 222 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 223 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 224 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 225 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 226 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 227 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 228 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 229 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 230 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 231 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 232 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 233 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 234 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 235 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 236 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 237 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 238 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 239 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 240 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 241 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 242 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 243 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 244 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 245 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 246 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 247 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 248 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 249 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 250 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 251 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 252 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 253 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 254 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 255 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 256 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 257 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 258 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 259 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 260 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 261 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 262 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 263 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 264 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 265 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 266 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 267 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 268 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 269 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 270 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 271 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 272 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 273 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 274 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 275 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 276 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 277 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 278 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 279 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 280 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 281 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 282 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 283 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 284 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 285 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 286 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 287 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 288 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 289 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 290 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 291 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 292 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 293 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 294 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 295 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 296 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 297 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 298 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 299 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 300 "D"     "D"      "D"      "D"      "D"      "D"      "D"
df_m.M_altA <- model_results_f_altA[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M_altA %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 10400       1
## 
## [[2]]
##  cycle 1     n percent
##        D   143 0.01375
##      MPD 10257 0.98625
## 
## [[3]]
##  cycle 2    n    percent
##      APD  310 0.02980769
##        D  671 0.06451923
##      MPD 9419 0.90567308
## 
## [[4]]
##  cycle 3    n    percent
##      APD  691 0.06644231
##        D 1173 0.11278846
##      MPD 8536 0.82076923
## 
## [[5]]
##  cycle 4    n    percent
##      APD  872 0.08384615
##        D 2224 0.21384615
##      MPD 7304 0.70230769
## 
## [[6]]
##  cycle 5    n    percent
##      APD  964 0.09269231
##        D 3507 0.33721154
##      MPD 5929 0.57009615
## 
## [[7]]
##  cycle 6    n    percent
##      APD  844 0.08115385
##        D 5168 0.49692308
##      MPD 4388 0.42192308
## 
## [[8]]
##  cycle 7    n    percent
##      APD  545 0.05240385
##        D 7029 0.67586538
##      MPD 2826 0.27173077
## 
## [[9]]
##  cycle 8    n    percent
##      APD  240 0.02307692
##        D 8661 0.83278846
##      MPD 1499 0.14413462
## 
## [[10]]
##  cycle 9    n     percent
##      APD   63 0.006057692
##        D 9761 0.938557692
##      MPD  576 0.055384615
## 
## [[11]]
##  cycle 10     n     percent
##       APD    16 0.001538462
##         D 10196 0.980384615
##       MPD   188 0.018076923
## 
## [[12]]
##  cycle 11     n      percent
##       APD     4 0.0003846154
##         D 10329 0.9931730769
##       MPD    67 0.0064423077
## 
## [[13]]
##  cycle 12     n      percent
##       APD     3 0.0002884615
##         D 10373 0.9974038462
##       MPD    24 0.0023076923
## 
## [[14]]
##  cycle 13     n     percent
##         D 10389 0.998942308
##       MPD    11 0.001057692
## 
## [[15]]
##  cycle 14     n      percent
##         D 10394 0.9994230769
##       MPD     6 0.0005769231
#Transition costs
transition_costs_f_alt <-
  transition_costs_f_alt %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
    "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")

final_cost_f_altA <- map(
    model_results_f_altA,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_f_alt
      )
  )
 

final_cost_f2_altA <-
  map(
    final_cost_f_altA,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
    filter(cycle != "cycle 15")
  )
final_cost_f2_altA
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 177882795.
##  4 cycle 3  10400 167187184.
##  5 cycle 4  10400 191786997.
##  6 cycle 5  10400 151697542.
##  7 cycle 6  10400 122587410.
##  8 cycle 7  10400  53823922.
##  9 cycle 8  10400  11572682.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 175386695.
##  4 cycle 3  10400 166209892.
##  5 cycle 4  10400 188287770.
##  6 cycle 5  10400 149303320.
##  7 cycle 6  10400 119269752.
##  8 cycle 7  10400  54056604.
##  9 cycle 8  10400  12911991.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 177768048.
##  4 cycle 3  10400 168044553.
##  5 cycle 4  10400 189730000.
##  6 cycle 5  10400 151175606.
##  7 cycle 6  10400 119767829.
##  8 cycle 7  10400  53760735.
##  9 cycle 8  10400  12558248.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 177535721.
##  4 cycle 3  10400 168314443.
##  5 cycle 4  10400 190343938.
##  6 cycle 5  10400 151285074.
##  7 cycle 6  10400 122131408.
##  8 cycle 7  10400  53795676.
##  9 cycle 8  10400  12028278.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    128629.
## 12 cycle 11 10400         0 
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 176945391.
##  4 cycle 3  10400 166981076.
##  5 cycle 4  10400 188440719.
##  6 cycle 5  10400 150418763.
##  7 cycle 6  10400 119246555.
##  8 cycle 7  10400  53685655.
##  9 cycle 8  10400  12985013.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 177917402.
##  4 cycle 3  10400 166903328.
##  5 cycle 4  10400 191687033.
##  6 cycle 5  10400 152401381.
##  7 cycle 6  10400 122319686.
##  8 cycle 7  10400  55005147.
##  9 cycle 8  10400  12109347.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 177353178.
##  4 cycle 3  10400 165616612.
##  5 cycle 4  10400 188603264.
##  6 cycle 5  10400 150571768.
##  7 cycle 6  10400 119674270.
##  8 cycle 7  10400  54121274.
##  9 cycle 8  10400  13209602.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249629414.
##  3 cycle 2  10400 179017109.
##  4 cycle 3  10400 167055664.
##  5 cycle 4  10400 190286396.
##  6 cycle 5  10400 151294989.
##  7 cycle 6  10400 122871630.
##  8 cycle 7  10400  55072051.
##  9 cycle 8  10400  13170558.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 177184396.
##  4 cycle 3  10400 166556215.
##  5 cycle 4  10400 189937419.
##  6 cycle 5  10400 150948035.
##  7 cycle 6  10400 120632666.
##  8 cycle 7  10400  53988210.
##  9 cycle 8  10400  12393943.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250506674.
##  3 cycle 2  10400 178761915.
##  4 cycle 3  10400 168494458.
##  5 cycle 4  10400 192207495.
##  6 cycle 5  10400 152879779.
##  7 cycle 6  10400 123482724.
##  8 cycle 7  10400  56285237.
##  9 cycle 8  10400  12652152.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 177643384.
##  4 cycle 3  10400 168915628.
##  5 cycle 4  10400 190894948.
##  6 cycle 5  10400 150912267.
##  7 cycle 6  10400 121103810.
##  8 cycle 7  10400  54134655.
##  9 cycle 8  10400  11766453.
## 10 cycle 9  10400    713305.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 177119433.
##  4 cycle 3  10400 167142114.
##  5 cycle 4  10400 190916902.
##  6 cycle 5  10400 150483412.
##  7 cycle 6  10400 120908251.
##  8 cycle 7  10400  54551684.
##  9 cycle 8  10400  12716591.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 177510625.
##  4 cycle 3  10400 168692654.
##  5 cycle 4  10400 190372864.
##  6 cycle 5  10400 150120088.
##  7 cycle 6  10400 119383156.
##  8 cycle 7  10400  53057506.
##  9 cycle 8  10400  12800461.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249434467.
##  3 cycle 2  10400 176894189.
##  4 cycle 3  10400 166021971.
##  5 cycle 4  10400 189696035.
##  6 cycle 5  10400 149543834.
##  7 cycle 6  10400 122218266.
##  8 cycle 7  10400  53499069.
##  9 cycle 8  10400  13341280.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 178052182.
##  4 cycle 3  10400 167815253.
##  5 cycle 4  10400 190638618.
##  6 cycle 5  10400 149603309.
##  7 cycle 6  10400 121201106.
##  8 cycle 7  10400  54537561.
##  9 cycle 8  10400  12907560.
## 10 cycle 9  10400    783466.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 178093872.
##  4 cycle 3  10400 167177958.
##  5 cycle 4  10400 189068774.
##  6 cycle 5  10400 150276537.
##  7 cycle 6  10400 120118674.
##  8 cycle 7  10400  55497259.
##  9 cycle 8  10400  13104490.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 176885689.
##  4 cycle 3  10400 166977386.
##  5 cycle 4  10400 189747398.
##  6 cycle 5  10400 151057503.
##  7 cycle 6  10400 123074856.
##  8 cycle 7  10400  54909998.
##  9 cycle 8  10400  13653356.
## 10 cycle 9  10400   1052417.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 176379145.
##  4 cycle 3  10400 165696736.
##  5 cycle 4  10400 190054124.
##  6 cycle 5  10400 150469188.
##  7 cycle 6  10400 118463098.
##  8 cycle 7  10400  53757762.
##  9 cycle 8  10400  12295071.
## 10 cycle 9  10400    643144.
## 11 cycle 10 10400    140322.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249726887.
##  3 cycle 2  10400 179731292.
##  4 cycle 3  10400 166856938.
##  5 cycle 4  10400 189332422.
##  6 cycle 5  10400 148933957.
##  7 cycle 6  10400 120411335.
##  8 cycle 7  10400  54462481.
##  9 cycle 8  10400  12186979.
## 10 cycle 9  10400    678224.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 176875167.
##  4 cycle 3  10400 166359334.
##  5 cycle 4  10400 189043921.
##  6 cycle 5  10400 149449006.
##  7 cycle 6  10400 120748454.
##  8 cycle 7  10400  53968879.
##  9 cycle 8  10400  12226023.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249751255.
##  3 cycle 2  10400 176595482.
##  4 cycle 3  10400 164802203.
##  5 cycle 4  10400 189313092.
##  6 cycle 5  10400 149512790.
##  7 cycle 6  10400 122123353.
##  8 cycle 7  10400  55200651.
##  9 cycle 8  10400  13431747.
## 10 cycle 9  10400    678224.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 177206658.
##  4 cycle 3  10400 167270468.
##  5 cycle 4  10400 190554911.
##  6 cycle 5  10400 149475292.
##  7 cycle 6  10400 120461077.
##  8 cycle 7  10400  54534588.
##  9 cycle 8  10400  12562678.
## 10 cycle 9  10400    631450.
## 11 cycle 10 10400    116935.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 175999891.
##  4 cycle 3  10400 165878065.
##  5 cycle 4  10400 186858691.
##  6 cycle 5  10400 146220349.
##  7 cycle 6  10400 116219817.
##  8 cycle 7  10400  52526734.
##  9 cycle 8  10400  11847343.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 177466508.
##  4 cycle 3  10400 168766452.
##  5 cycle 4  10400 191648338.
##  6 cycle 5  10400 151619533.
##  7 cycle 6  10400 121248272.
##  8 cycle 7  10400  54319753.
##  9 cycle 8  10400  11940791.
## 10 cycle 9  10400    666531.
## 11 cycle 10 10400    140322.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 176029237.
##  4 cycle 3  10400 165973476.
##  5 cycle 4  10400 187693681.
##  6 cycle 5  10400 147101750.
##  7 cycle 6  10400 120421966.
##  8 cycle 7  10400  54794769.
##  9 cycle 8  10400  12060269.
## 10 cycle 9  10400    654837.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 176190935.
##  4 cycle 3  10400 164803258.
##  5 cycle 4  10400 189357310.
##  6 cycle 5  10400 149990340.
##  7 cycle 6  10400 121196595.
##  8 cycle 7  10400  54435721.
##  9 cycle 8  10400  13346704.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 176163412.
##  4 cycle 3  10400 166859838.
##  5 cycle 4  10400 188876819.
##  6 cycle 5  10400 148803361.
##  7 cycle 6  10400 121497891.
##  8 cycle 7  10400  53786012.
##  9 cycle 8  10400  13335497.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 178198702.
##  4 cycle 3  10400 166856148.
##  5 cycle 4  10400 190393505.
##  6 cycle 5  10400 149700283.
##  7 cycle 6  10400 120141290.
##  8 cycle 7  10400  54961286.
##  9 cycle 8  10400  12214002.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 177000842.
##  4 cycle 3  10400 166083118.
##  5 cycle 4  10400 189485544.
##  6 cycle 5  10400 149209374.
##  7 cycle 6  10400 120902325.
##  8 cycle 7  10400  53279776.
##  9 cycle 8  10400  12301489.
## 10 cycle 9  10400    689918.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 178331867.
##  4 cycle 3  10400 168523186.
##  5 cycle 4  10400 191828280.
##  6 cycle 5  10400 152404825.
##  7 cycle 6  10400 122490114.
##  8 cycle 7  10400  53462639.
##  9 cycle 8  10400  12935755.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    152016.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249507572.
##  3 cycle 2  10400 176606816.
##  4 cycle 3  10400 166004839.
##  5 cycle 4  10400 187495858.
##  6 cycle 5  10400 147800847.
##  7 cycle 6  10400 119433092.
##  8 cycle 7  10400  52726701.
##  9 cycle 8  10400  12248615.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 177961519.
##  4 cycle 3  10400 168147079.
##  5 cycle 4  10400 188571404.
##  6 cycle 5  10400 148623188.
##  7 cycle 6  10400 119408542.
##  8 cycle 7  10400  54356921.
##  9 cycle 8  10400  12796845.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 177395878.
##  4 cycle 3  10400 166132669.
##  5 cycle 4  10400 190620427.
##  6 cycle 5  10400 151604011.
##  7 cycle 6  10400 121193051.
##  8 cycle 7  10400  55367915.
##  9 cycle 8  10400  13224425.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249873097.
##  3 cycle 2  10400 176787943.
##  4 cycle 3  10400 167674247.
##  5 cycle 4  10400 190083706.
##  6 cycle 5  10400 151157073.
##  7 cycle 6  10400 122015233.
##  8 cycle 7  10400  54298937.
##  9 cycle 8  10400  11766632.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 177453757.
##  4 cycle 3  10400 167046438.
##  5 cycle 4  10400 187608490.
##  6 cycle 5  10400 148523669.
##  7 cycle 6  10400 117184852.
##  8 cycle 7  10400  52458350.
##  9 cycle 8  10400  11806132.
## 10 cycle 9  10400    701611.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 176458880.
##  4 cycle 3  10400 166217008.
##  5 cycle 4  10400 188935017.
##  6 cycle 5  10400 147760754.
##  7 cycle 6  10400 119133538.
##  8 cycle 7  10400  52178838.
##  9 cycle 8  10400  11649598.
## 10 cycle 9  10400    619756.
## 11 cycle 10 10400    152016.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 177798405.
##  4 cycle 3  10400 166884876.
##  5 cycle 4  10400 190237795.
##  6 cycle 5  10400 151666082.
##  7 cycle 6  10400 121667932.
##  8 cycle 7  10400  55182070.
##  9 cycle 8  10400  12645735.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 178476564.
##  4 cycle 3  10400 168321293.
##  5 cycle 4  10400 190843102.
##  6 cycle 5  10400 152314322.
##  7 cycle 6  10400 120909412.
##  8 cycle 7  10400  53279032.
##  9 cycle 8  10400  12292448.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    374193.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 178481825.
##  4 cycle 3  10400 168406687.
##  5 cycle 4  10400 188303372.
##  6 cycle 5  10400 147557755.
##  7 cycle 6  10400 119269558.
##  8 cycle 7  10400  53406149.
##  9 cycle 8  10400  12101936.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 179476098.
##  4 cycle 3  10400 168941721.
##  5 cycle 4  10400 191168193.
##  6 cycle 5  10400 151909607.
##  7 cycle 6  10400 120991179.
##  8 cycle 7  10400  54101946.
##  9 cycle 8  10400  12041651.
## 10 cycle 9  10400    572982.
## 11 cycle 10 10400     70161.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 178280259.
##  4 cycle 3  10400 166302402.
##  5 cycle 4  10400 189659135.
##  6 cycle 5  10400 149792083.
##  7 cycle 6  10400 120792657.
##  8 cycle 7  10400  52276219.
##  9 cycle 8  10400  12473025.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 177980948.
##  4 cycle 3  10400 166769173.
##  5 cycle 4  10400 190181082.
##  6 cycle 5  10400 151411361.
##  7 cycle 6  10400 122210599.
##  8 cycle 7  10400  54795516.
##  9 cycle 8  10400  13019447.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249361362.
##  3 cycle 2  10400 174373399.
##  4 cycle 3  10400 164739476.
##  5 cycle 4  10400 187976176.
##  6 cycle 5  10400 147905141.
##  7 cycle 6  10400 119202933.
##  8 cycle 7  10400  53664839.
##  9 cycle 8  10400  12611121.
## 10 cycle 9  10400    678224.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250555411.
##  3 cycle 2  10400 177710170.
##  4 cycle 3  10400 168188459.
##  5 cycle 4  10400 189878083.
##  6 cycle 5  10400 149796392.
##  7 cycle 6  10400 120126535.
##  8 cycle 7  10400  53910900.
##  9 cycle 8  10400  12025476.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 177050220.
##  4 cycle 3  10400 164857024.
##  5 cycle 4  10400 188336371.
##  6 cycle 5  10400 148454677.
##  7 cycle 6  10400 120145220.
##  8 cycle 7  10400  54699620.
##  9 cycle 8  10400  13460399.
## 10 cycle 9  10400   1087497.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 177546649.
##  4 cycle 3  10400 167658961.
##  5 cycle 4  10400 188589768.
##  6 cycle 5  10400 149064986.
##  7 cycle 6  10400 119456676.
##  8 cycle 7  10400  52835235.
##  9 cycle 8  10400  12292269.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 175882311.
##  4 cycle 3  10400 166361703.
##  5 cycle 4  10400 189588925.
##  6 cycle 5  10400 148857663.
##  7 cycle 6  10400 119503261.
##  8 cycle 7  10400  53664098.
##  9 cycle 8  10400  11769075.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 177524387.
##  4 cycle 3  10400 166798691.
##  5 cycle 4  10400 189929445.
##  6 cycle 5  10400 151106215.
##  7 cycle 6  10400 120733506.
##  8 cycle 7  10400  53832844.
##  9 cycle 8  10400  12548670.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 178399257.
##  4 cycle 3  10400 166694320.
##  5 cycle 4  10400 189322516.
##  6 cycle 5  10400 151537216.
##  7 cycle 6  10400 121388030.
##  8 cycle 7  10400  54924862.
##  9 cycle 8  10400  11749643.
## 10 cycle 9  10400    596369.
## 11 cycle 10 10400    116935.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 176445118.
##  4 cycle 3  10400 165272399.
##  5 cycle 4  10400 189277332.
##  6 cycle 5  10400 150184721.
##  7 cycle 6  10400 121551177.
##  8 cycle 7  10400  54754630.
##  9 cycle 8  10400  12702403.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 177091911.
##  4 cycle 3  10400 166065461.
##  5 cycle 4  10400 188880409.
##  6 cycle 5  10400 147727564.
##  7 cycle 6  10400 119455515.
##  8 cycle 7  10400  54136882.
##  9 cycle 8  10400  12571083.
## 10 cycle 9  10400    713305.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 178297260.
##  4 cycle 3  10400 165707542.
##  5 cycle 4  10400 188690560.
##  6 cycle 5  10400 149401576.
##  7 cycle 6  10400 120926549.
##  8 cycle 7  10400  54978379.
##  9 cycle 8  10400  12368272.
## 10 cycle 9  10400    724998.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249775624.
##  3 cycle 2  10400 177481280.
##  4 cycle 3  10400 168816527.
##  5 cycle 4  10400 189901486.
##  6 cycle 5  10400 150190344.
##  7 cycle 6  10400 121319989.
##  8 cycle 7  10400  54194871.
##  9 cycle 8  10400  12680805.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249385730.
##  3 cycle 2  10400 176396344.
##  4 cycle 3  10400 167570400.
##  5 cycle 4  10400 189578674.
##  6 cycle 5  10400 150638148.
##  7 cycle 6  10400 123253725.
##  8 cycle 7  10400  54979870.
##  9 cycle 8  10400  13262019.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 179481359.
##  4 cycle 3  10400 168774358.
##  5 cycle 4  10400 191008236.
##  6 cycle 5  10400 150704095.
##  7 cycle 6  10400 120532214.
##  8 cycle 7  10400  53328096.
##  9 cycle 8  10400  12331313.
## 10 cycle 9  10400    666531.
## 11 cycle 10 10400    140322.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 176839144.
##  4 cycle 3  10400 167510840.
##  5 cycle 4  10400 191951475.
##  6 cycle 5  10400 153693536.
##  7 cycle 6  10400 122374326.
##  8 cycle 7  10400  55942544.
##  9 cycle 8  10400  13927560.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 177123277.
##  4 cycle 3  10400 166955773.
##  5 cycle 4  10400 189477743.
##  6 cycle 5  10400 149611062.
##  7 cycle 6  10400 119098550.
##  8 cycle 7  10400  53930972.
##  9 cycle 8  10400  13392166.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250457938.
##  3 cycle 2  10400 176771754.
##  4 cycle 3  10400 166490588.
##  5 cycle 4  10400 190675690.
##  6 cycle 5  10400 151335066.
##  7 cycle 6  10400 121971030.
##  8 cycle 7  10400  56194544.
##  9 cycle 8  10400  13165769.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 177113162.
##  4 cycle 3  10400 166863529.
##  5 cycle 4  10400 190288502.
##  6 cycle 5  10400 150086466.
##  7 cycle 6  10400 118772062.
##  8 cycle 7  10400  53236658.
##  9 cycle 8  10400  12819894.
## 10 cycle 9  10400    783466.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 178093872.
##  4 cycle 3  10400 166608925.
##  5 cycle 4  10400 190324745.
##  6 cycle 5  10400 149576159.
##  7 cycle 6  10400 120235816.
##  8 cycle 7  10400  53537725.
##  9 cycle 8  10400  11779288.
## 10 cycle 9  10400    783466.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 176971496.
##  4 cycle 3  10400 166895682.
##  5 cycle 4  10400 189186135.
##  6 cycle 5  10400 151332937.
##  7 cycle 6  10400 121529528.
##  8 cycle 7  10400  54550204.
##  9 cycle 8  10400  13098529.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 176713469.
##  4 cycle 3  10400 166870644.
##  5 cycle 4  10400 191832181.
##  6 cycle 5  10400 152427666.
##  7 cycle 6  10400 121438739.
##  8 cycle 7  10400  54003074.
##  9 cycle 8  10400  11904727.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250652885.
##  3 cycle 2  10400 175931085.
##  4 cycle 3  10400 166265768.
##  5 cycle 4  10400 190269172.
##  6 cycle 5  10400 151379885.
##  7 cycle 6  10400 122886771.
##  8 cycle 7  10400  55928413.
##  9 cycle 8  10400  13993986.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 176512914.
##  4 cycle 3  10400 165146415.
##  5 cycle 4  10400 189482783.
##  6 cycle 5  10400 150019221.
##  7 cycle 6  10400 122042167.
##  8 cycle 7  10400  54974667.
##  9 cycle 8  10400  12841493.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 178067766.
##  4 cycle 3  10400 167683208.
##  5 cycle 4  10400 190280217.
##  6 cycle 5  10400 149901984.
##  7 cycle 6  10400 122716150.
##  8 cycle 7  10400  55321078.
##  9 cycle 8  10400  13718788.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 175455501.
##  4 cycle 3  10400 164767414.
##  5 cycle 4  10400 190571998.
##  6 cycle 5  10400 150851045.
##  7 cycle 6  10400 121409291.
##  8 cycle 7  10400  55846640.
##  9 cycle 8  10400  13567856.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 176842988.
##  4 cycle 3  10400 166494803.
##  5 cycle 4  10400 189812084.
##  6 cycle 5  10400 148848629.
##  7 cycle 6  10400 118444800.
##  8 cycle 7  10400  53599426.
##  9 cycle 8  10400  12175772.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 176536187.
##  4 cycle 3  10400 166201721.
##  5 cycle 4  10400 189240259.
##  6 cycle 5  10400 149188695.
##  7 cycle 6  10400 118877412.
##  8 cycle 7  10400  53832847.
##  9 cycle 8  10400  12479443.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 177965364.
##  4 cycle 3  10400 167244375.
##  5 cycle 4  10400 190641552.
##  6 cycle 5  10400 150626070.
##  7 cycle 6  10400 121224689.
##  8 cycle 7  10400  54222372.
##  9 cycle 8  10400  12787268.
## 10 cycle 9  10400    689918.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     11694.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 176627661.
##  4 cycle 3  10400 166666382.
##  5 cycle 4  10400 189038709.
##  6 cycle 5  10400 148697769.
##  7 cycle 6  10400 119681551.
##  8 cycle 7  10400  53553333.
##  9 cycle 8  10400  12329326.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 176708208.
##  4 cycle 3  10400 165727575.
##  5 cycle 4  10400 189466214.
##  6 cycle 5  10400 148038749.
##  7 cycle 6  10400 122119810.
##  8 cycle 7  10400  55332233.
##  9 cycle 8  10400  13660767.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250482306.
##  3 cycle 2  10400 176028831.
##  4 cycle 3  10400 165430271.
##  5 cycle 4  10400 188273134.
##  6 cycle 5  10400 149470134.
##  7 cycle 6  10400 119574011.
##  8 cycle 7  10400  53706474.
##  9 cycle 8  10400  12674387.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 176260752.
##  4 cycle 3  10400 166144789.
##  5 cycle 4  10400 187508526.
##  6 cycle 5  10400 147389244.
##  7 cycle 6  10400 118893134.
##  8 cycle 7  10400  54439441.
##  9 cycle 8  10400  13148780.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 177791321.
##  4 cycle 3  10400 167581737.
##  5 cycle 4  10400 189093006.
##  6 cycle 5  10400 149044723.
##  7 cycle 6  10400 120017641.
##  8 cycle 7  10400  54780646.
##  9 cycle 8  10400  12291276.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 176777015.
##  4 cycle 3  10400 166241255.
##  5 cycle 4  10400 190380837.
##  6 cycle 5  10400 149626135.
##  7 cycle 6  10400 122142619.
##  8 cycle 7  10400  54465455.
##  9 cycle 8  10400  12650165.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 176357894.
##  4 cycle 3  10400 167105215.
##  5 cycle 4  10400 189228730.
##  6 cycle 5  10400 149391244.
##  7 cycle 6  10400 119038431.
##  8 cycle 7  10400  52751973.
##  9 cycle 8  10400  12009839.
## 10 cycle 9  10400    549595.
## 11 cycle 10 10400    152016.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 176652351.
##  4 cycle 3  10400 165194386.
##  5 cycle 4  10400 190538343.
##  6 cycle 5  10400 149724406.
##  7 cycle 6  10400 121457425.
##  8 cycle 7  10400  54689956.
##  9 cycle 8  10400  12615910.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250262991.
##  3 cycle 2  10400 177480269.
##  4 cycle 3  10400 166776289.
##  5 cycle 4  10400 189934968.
##  6 cycle 5  10400 150923480.
##  7 cycle 6  10400 121960980.
##  8 cycle 7  10400  53536239.
##  9 cycle 8  10400  12646370.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 177635290.
##  4 cycle 3  10400 166695375.
##  5 cycle 4  10400 190147910.
##  6 cycle 5  10400 150405836.
##  7 cycle 6  10400 122576005.
##  8 cycle 7  10400  54345775.
##  9 cycle 8  10400  12469230.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    140322.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 177560816.
##  4 cycle 3  10400 166739655.
##  5 cycle 4  10400 190717629.
##  6 cycle 5  10400 151225166.
##  7 cycle 6  10400 121625470.
##  8 cycle 7  10400  54072210.
##  9 cycle 8  10400  12363304.
## 10 cycle 9  10400    713305.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 176107555.
##  4 cycle 3  10400 165006200.
##  5 cycle 4  10400 189134945.
##  6 cycle 5  10400 149514520.
##  7 cycle 6  10400 120904901.
##  8 cycle 7  10400  54864649.
##  9 cycle 8  10400  13466996.
## 10 cycle 9  10400   1087497.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 176968663.
##  4 cycle 3  10400 166720413.
##  5 cycle 4  10400 189396798.
##  6 cycle 5  10400 148994730.
##  7 cycle 6  10400 119685288.
##  8 cycle 7  10400  54538305.
##  9 cycle 8  10400  12713789.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 177151612.
##  4 cycle 3  10400 166245211.
##  5 cycle 4  10400 190289330.
##  6 cycle 5  10400 150923031.
##  7 cycle 6  10400 120655476.
##  8 cycle 7  10400  55144899.
##  9 cycle 8  10400  12286666.
## 10 cycle 9  10400    619756.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 178077277.
##  4 cycle 3  10400 167254126.
##  5 cycle 4  10400 188338787.
##  6 cycle 5  10400 148100404.
##  7 cycle 6  10400 119905590.
##  8 cycle 7  10400  52934851.
##  9 cycle 8  10400  12744249.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 178101362.
##  4 cycle 3  10400 166686680.
##  5 cycle 4  10400 191276614.
##  6 cycle 5  10400 151268271.
##  7 cycle 6  10400 121633524.
##  8 cycle 7  10400  54536079.
##  9 cycle 8  10400  13134136.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 176953485.
##  4 cycle 3  10400 166888566.
##  5 cycle 4  10400 189081304.
##  6 cycle 5  10400 149389082.
##  7 cycle 6  10400 121729659.
##  8 cycle 7  10400  55445222.
##  9 cycle 8  10400  12916144.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 178057849.
##  4 cycle 3  10400 166658476.
##  5 cycle 4  10400 189246921.
##  6 cycle 5  10400 149460219.
##  7 cycle 6  10400 118962336.
##  8 cycle 7  10400  53005469.
##  9 cycle 8  10400  12101936.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    140322.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 179708425.
##  4 cycle 3  10400 168421449.
##  5 cycle 4  10400 190498026.
##  6 cycle 5  10400 149327460.
##  7 cycle 6  10400 118806082.
##  8 cycle 7  10400  54324956.
##  9 cycle 8  10400  13026859.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249483204.
##  3 cycle 2  10400 178330450.
##  4 cycle 3  10400 166981866.
##  5 cycle 4  10400 189663208.
##  6 cycle 5  10400 149786477.
##  7 cycle 6  10400 120866563.
##  8 cycle 7  10400  54842345.
##  9 cycle 8  10400  12521647.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 176129410.
##  4 cycle 3  10400 165171453.
##  5 cycle 4  10400 189297317.
##  6 cycle 5  10400 147687071.
##  7 cycle 6  10400 118054843.
##  8 cycle 7  10400  53291669.
##  9 cycle 8  10400  12870503.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 176327944.
##  4 cycle 3  10400 166015911.
##  5 cycle 4  10400 190784594.
##  6 cycle 5  10400 152054411.
##  7 cycle 6  10400 122001701.
##  8 cycle 7  10400  54489241.
##  9 cycle 8  10400  11647333.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 177223659.
##  4 cycle 3  10400 166701966.
##  5 cycle 4  10400 191156008.
##  6 cycle 5  10400 149970510.
##  7 cycle 6  10400 121613291.
##  8 cycle 7  10400  54495932.
##  9 cycle 8  10400  12170348.
## 10 cycle 9  10400    724998.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 177405796.
##  4 cycle 3  10400 166535393.
##  5 cycle 4  10400 190543210.
##  6 cycle 5  10400 151687210.
##  7 cycle 6  10400 122070067.
##  8 cycle 7  10400  54521956.
##  9 cycle 8  10400  13176976.
## 10 cycle 9  10400   1040723.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 177107089.
##  4 cycle 3  10400 167390916.
##  5 cycle 4  10400 189468803.
##  6 cycle 5  10400 149880007.
##  7 cycle 6  10400 119437990.
##  8 cycle 7  10400  52525995.
##  9 cycle 8  10400  12477635.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249653782.
##  3 cycle 2  10400 177028365.
##  4 cycle 3  10400 165768423.
##  5 cycle 4  10400 188269890.
##  6 cycle 5  10400 148059445.
##  7 cycle 6  10400 119592309.
##  8 cycle 7  10400  53341472.
##  9 cycle 8  10400  11989234.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 178912280.
##  4 cycle 3  10400 167160301.
##  5 cycle 4  10400 190574759.
##  6 cycle 5  10400 150579937.
##  7 cycle 6  10400 122995472.
##  8 cycle 7  10400  54519716.
##  9 cycle 8  10400  12531225.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 176229792.
##  4 cycle 3  10400 168243545.
##  5 cycle 4  10400 190288191.
##  6 cycle 5  10400 151650993.
##  7 cycle 6  10400 124225716.
##  8 cycle 7  10400  56392277.
##  9 cycle 8  10400  12878093.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     23387.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 176949235.
##  4 cycle 3  10400 166384106.
##  5 cycle 4  10400 189328039.
##  6 cycle 5  10400 148664580.
##  7 cycle 6  10400 118884499.
##  8 cycle 7  10400  53379386.
##  9 cycle 8  10400  12240846.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 177665646.
##  4 cycle 3  10400 167415164.
##  5 cycle 4  10400 189302840.
##  6 cycle 5  10400 148828366.
##  7 cycle 6  10400 118923224.
##  8 cycle 7  10400  52957149.
##  9 cycle 8  10400  13195415.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 177472175.
##  4 cycle 3  10400 167304997.
##  5 cycle 4  10400 190746555.
##  6 cycle 5  10400 150217045.
##  7 cycle 6  10400 121651629.
##  8 cycle 7  10400  55353040.
##  9 cycle 8  10400  12832273.
## 10 cycle 9  10400    631450.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0

The variability of costs over 30 simulations is observed through a box plot:

#Males
final_cost_m2_alt_combinedA <- bind_rows(final_cost_m2_altA)

final_cost_m2_alt_combinedA$cycle <- factor(final_cost_m2_alt_combinedA$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_m_altA <- ggplot(final_cost_m2_alt_combinedA, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario A2 (Males)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m_altA

#Females
final_cost_f2_alt_combinedA <- bind_rows(final_cost_f2_altA)

final_cost_f2_alt_combinedA$cycle <- factor(final_cost_f2_alt_combinedA$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_f_altA <- ggplot(final_cost_f2_alt_combinedA, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario A2 (Females)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f_altA

The graphs showcasing costs over cycles are:

#Averaging costs across simulations
#Males
combined_costs_m_altA <- map_df(final_cost_m2_altA, ~ .x)
mean_costs_per_cycle_m_altA <- combined_costs_m_altA %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_m_altA)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0     440865997.
##  2 cycle 1     284876172.
##  3 cycle 2     249728670.
##  4 cycle 3     278128114.
##  5 cycle 4     236299323.
##  6 cycle 5     158193238.
##  7 cycle 6      90246008.
##  8 cycle 7      29456225.
##  9 cycle 8       7503156.
## 10 cycle 9       1643781.
## 11 cycle 10       337794.
## 12 cycle 11        71245.
## 13 cycle 12        14808.
## 14 cycle 13         3051.
## 15 cycle 14          381.
#Females
combined_costs_f_altA <- map_df(final_cost_f2_altA, ~ .x)
mean_costs_per_cycle_f_altA <- combined_costs_f_altA %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_f_altA)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0     261295475.
##  2 cycle 1     249974470.
##  3 cycle 2     177276862.
##  4 cycle 3     166804471.
##  5 cycle 4     189768014.
##  6 cycle 5     150014118.
##  7 cycle 6     120728312.
##  8 cycle 7      54213065.
##  9 cycle 8      12624396.
## 10 cycle 9        818195.
## 11 cycle 10       219721.
## 12 cycle 11        59637.
## 13 cycle 12        15903.
## 14 cycle 13         5262.
## 15 cycle 14         1754.
#Graphs
#Males
graph1_altA <- ggplot(data = mean_costs_per_cycle_m_altA %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "turquoise") +
  ggtitle("Average total costs from microsimulation, alternative scenario A2 (Males)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m_alt$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
  options(scipen=999)
  
#Females
graph2_altA <- ggplot(data = mean_costs_per_cycle_f_altA %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "pink") +
  ggtitle("Average total costs from microsimulation, alternative scenario A2 (Females)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f_alt$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
  options(scipen=999)

graph1_altA

graph2_altA

Let’s compare graphs across scenarios:

#Males
mean_costs_combined_mA <- mean_costs_per_cycle_m %>%
  rename(avg_tot_costs_baseline = avg_tot_costs) %>%
  inner_join(mean_costs_per_cycle_m_altA %>%
               rename(avg_tot_costs_alt = avg_tot_costs),
             by = "cycle") %>%
  mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>% 
  pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
               names_to = "Scenario", values_to = "avg_tot_costs") %>%
  mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative A2", "extra_cost" = "Extra cost of baseline")) %>% 
  filter(Scenario != "Baseline") %>% 
  mutate(
    Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
  )

graph_combined_mA <- ggplot(data = mean_costs_combined_mA, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
  geom_col(data = subset(mean_costs_combined_mA, Scenario == "Alternative A2"), fill = "blue", width = 0.4) +
  geom_col(data = subset(mean_costs_combined_mA, Scenario == "Extra cost of baseline"),
           aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")), 
           width = 0.4) +
  scale_fill_manual(name = "Gains/losses", values = c("Alternative A2" = "blue", "Loss" = "red", "Gain" = "green")) +
  ggtitle("Comparison of average total costs of alternative scenario A2 wrt baseline scenario (Males)") +
  xlab("Cycle") +
  ylab("Cost") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
  scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_m$avg_tot_costs), max(mean_costs_combined_m$avg_tot_costs)))
graph_combined_mA

#Females
mean_costs_combined_fA <- mean_costs_per_cycle_f %>%
  rename(avg_tot_costs_baseline = avg_tot_costs) %>%
  inner_join(mean_costs_per_cycle_f_altA %>%
               rename(avg_tot_costs_alt = avg_tot_costs),
             by = "cycle") %>%
  mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>% 
  pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
               names_to = "Scenario", values_to = "avg_tot_costs") %>%
  mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative A2", "extra_cost" = "Extra cost of baseline")) %>% 
  filter(Scenario != "Baseline") %>% 
  mutate(
    Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
  )

graph_combined_fA <- ggplot(data = mean_costs_combined_fA, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
  geom_col(data = subset(mean_costs_combined_fA, Scenario == "Alternative A2"), fill = "pink", width = 0.4) +
  geom_col(data = subset(mean_costs_combined_fA, Scenario == "Extra cost of baseline"),
           aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")), 
           width = 0.4) +
  scale_fill_manual(name = "Gains/losses", values = c("Alternative A2" = "pink", "Loss" = "red", "Gain" = "green")) +
  ggtitle("Comparison of average total costs of alternative scenario A2 wrt baseline scenario (Females)") +
  xlab("Cycle") +
  ylab("Cost") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
  scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_f$avg_tot_costs), max(mean_costs_combined_f$avg_tot_costs)))
graph_combined_fA

This scenario is characterized by more significant financial gains compared to the situation of the previous scenario due to a higher mortality.

Discounted costs are:

discounted_costs_m_altA <-
  map(final_cost_m2_altA, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_m_altA
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       195225540.
##  4 cycle 3  15600       192486200.
##  5 cycle 4  15600       144931312.
##  6 cycle 5  15600        85701631.
##  7 cycle 6  15600        43350621.
##  8 cycle 7  15600        12071344.
##  9 cycle 8  15600         2768711.
## 10 cycle 9  15600          558580.
## 11 cycle 10 15600          163020.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       195630829.
##  4 cycle 3  15600       191988359.
##  5 cycle 4  15600       144269140.
##  6 cycle 5  15600        85170852.
##  7 cycle 6  15600        43298483.
##  8 cycle 7  15600        12797431.
##  9 cycle 8  15600         2944864.
## 10 cycle 9  15600          502094.
## 11 cycle 10 15600          147925.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       194872490.
##  4 cycle 3  15600       190799832.
##  5 cycle 4  15600       143605981.
##  6 cycle 5  15600        84447485.
##  7 cycle 6  15600        42494978.
##  8 cycle 7  15600        12162504.
##  9 cycle 8  15600         2761009.
## 10 cycle 9  15600          537659.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252006927.
##  3 cycle 2  15600       194292907.
##  4 cycle 3  15600       191854340.
##  5 cycle 4  15600       144308639.
##  6 cycle 5  15600        85393916.
##  7 cycle 6  15600        43193941.
##  8 cycle 7  15600        12421339.
##  9 cycle 8  15600         2863384.
## 10 cycle 9  15600          629710.
## 11 cycle 10 15600          187171.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       194341832.
##  4 cycle 3  15600       190608873.
##  5 cycle 4  15600       142578004.
##  6 cycle 5  15600        84946741.
##  7 cycle 6  15600        43004985.
##  8 cycle 7  15600        12440720.
##  9 cycle 8  15600         2732682.
## 10 cycle 9  15600          537659.
## 11 cycle 10 15600          138869.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251973685.
##  3 cycle 2  15600       195788943.
##  4 cycle 3  15600       192522998.
##  5 cycle 4  15600       144409376.
##  6 cycle 5  15600        85438809.
##  7 cycle 6  15600        43248320.
##  8 cycle 7  15600        12490071.
##  9 cycle 8  15600         2772467.
## 10 cycle 9  15600          543936.
## 11 cycle 10 15600          147925.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252605281.
##  3 cycle 2  15600       196876506.
##  4 cycle 3  15600       194876586.
##  5 cycle 4  15600       146972121.
##  6 cycle 5  15600        86941731.
##  7 cycle 6  15600        44500500.
##  8 cycle 7  15600        13303699.
##  9 cycle 8  15600         2920404.
## 10 cycle 9  15600          508371.
## 11 cycle 10 15600          129812.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       193624266.
##  4 cycle 3  15600       191052230.
##  5 cycle 4  15600       142982114.
##  6 cycle 5  15600        85339429.
##  7 cycle 6  15600        42924287.
##  8 cycle 7  15600        12193797.
##  9 cycle 8  15600         2850526.
## 10 cycle 9  15600          579501.
## 11 cycle 10 15600          117736.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       194520968.
##  4 cycle 3  15600       190912417.
##  5 cycle 4  15600       144448205.
##  6 cycle 5  15600        85842461.
##  7 cycle 6  15600        43433051.
##  8 cycle 7  15600        12776083.
##  9 cycle 8  15600         2810740.
## 10 cycle 9  15600          525107.
## 11 cycle 10 15600          156982.
## 12 cycle 11 15600           22418.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252405830.
##  3 cycle 2  15600       194698577.
##  4 cycle 3  15600       191055134.
##  5 cycle 4  15600       145016266.
##  6 cycle 5  15600        86481520.
##  7 cycle 6  15600        43328271.
##  8 cycle 7  15600        12743587.
##  9 cycle 8  15600         2802417.
## 10 cycle 9  15600          520923.
## 11 cycle 10 15600          166038.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       195285040.
##  4 cycle 3  15600       193859905.
##  5 cycle 4  15600       145438135.
##  6 cycle 5  15600        86280716.
##  7 cycle 6  15600        43443231.
##  8 cycle 7  15600        12507800.
##  9 cycle 8  15600         2759609.
## 10 cycle 9  15600          535567.
## 11 cycle 10 15600          141887.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       194361835.
##  4 cycle 3  15600       191305064.
##  5 cycle 4  15600       143737903.
##  6 cycle 5  15600        85314078.
##  7 cycle 6  15600        43203874.
##  8 cycle 7  15600        12622336.
##  9 cycle 8  15600         2726237.
## 10 cycle 9  15600          579501.
## 11 cycle 10 15600          208303.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251292226.
##  3 cycle 2  15600       194972505.
##  4 cycle 3  15600       192754542.
##  5 cycle 4  15600       145772549.
##  6 cycle 5  15600        85522755.
##  7 cycle 6  15600        43711153.
##  8 cycle 7  15600        12591043.
##  9 cycle 8  15600         2885409.
## 10 cycle 9  15600          535567.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252306104.
##  3 cycle 2  15600       196383815.
##  4 cycle 3  15600       193572148.
##  5 cycle 4  15600       143859217.
##  6 cycle 5  15600        85733163.
##  7 cycle 6  15600        43501589.
##  8 cycle 7  15600        12147361.
##  9 cycle 8  15600         2733271.
## 10 cycle 9  15600          527199.
## 11 cycle 10 15600          147925.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       195674911.
##  4 cycle 3  15600       192120650.
##  5 cycle 4  15600       145435984.
##  6 cycle 5  15600        84997453.
##  7 cycle 6  15600        42979161.
##  8 cycle 7  15600        12244472.
##  9 cycle 8  15600         2648433.
## 10 cycle 9  15600          520923.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       195037357.
##  4 cycle 3  15600       192015021.
##  5 cycle 4  15600       144026831.
##  6 cycle 5  15600        84457080.
##  7 cycle 6  15600        42461953.
##  8 cycle 7  15600        12181253.
##  9 cycle 8  15600         2956911.
## 10 cycle 9  15600          564856.
## 11 cycle 10 15600          181133.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       194291250.
##  4 cycle 3  15600       189988311.
##  5 cycle 4  15600       143088170.
##  6 cycle 5  15600        84686307.
##  7 cycle 6  15600        42052753.
##  8 cycle 7  15600        12188345.
##  9 cycle 8  15600         2584745.
## 10 cycle 9  15600          537659.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       196634046.
##  4 cycle 3  15600       192152234.
##  5 cycle 4  15600       144688684.
##  6 cycle 5  15600        86265987.
##  7 cycle 6  15600        43264954.
##  8 cycle 7  15600        12374842.
##  9 cycle 8  15600         2872041.
## 10 cycle 9  15600          566948.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       197197958.
##  4 cycle 3  15600       194798344.
##  5 cycle 4  15600       146633900.
##  6 cycle 5  15600        85909623.
##  7 cycle 6  15600        43145030.
##  8 cycle 7  15600        12568177.
##  9 cycle 8  15600         2794571.
## 10 cycle 9  15600          558580.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251724371.
##  3 cycle 2  15600       194975819.
##  4 cycle 3  15600       191182633.
##  5 cycle 4  15600       143585547.
##  6 cycle 5  15600        84212429.
##  7 cycle 6  15600        42476859.
##  8 cycle 7  15600        12844243.
##  9 cycle 8  15600         2995073.
## 10 cycle 9  15600          546028.
## 11 cycle 10 15600          153963.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251258984.
##  3 cycle 2  15600       194034011.
##  4 cycle 3  15600       189898771.
##  5 cycle 4  15600       143664369.
##  6 cycle 5  15600        85588882.
##  7 cycle 6  15600        44038157.
##  8 cycle 7  15600        12360331.
##  9 cycle 8  15600         2776223.
## 10 cycle 9  15600          491634.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       193356451.
##  4 cycle 3  15600       191745820.
##  5 cycle 4  15600       145086981.
##  6 cycle 5  15600        86328366.
##  7 cycle 6  15600        43677139.
##  8 cycle 7  15600        12512244.
##  9 cycle 8  15600         2701665.
## 10 cycle 9  15600          556488.
## 11 cycle 10 15600          172076.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       195279816.
##  4 cycle 3  15600       192280604.
##  5 cycle 4  15600       145861690.
##  6 cycle 5  15600        85536125.
##  7 cycle 6  15600        43063338.
##  8 cycle 7  15600        12635633.
##  9 cycle 8  15600         2799283.
## 10 cycle 9  15600          579501.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600           15608.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251674508.
##  3 cycle 2  15600       196715587.
##  4 cycle 3  15600       191487338.
##  5 cycle 4  15600       144975955.
##  6 cycle 5  15600        86454466.
##  7 cycle 6  15600        43387619.
##  8 cycle 7  15600        12481388.
##  9 cycle 8  15600         2699199.
## 10 cycle 9  15600          566948.
## 11 cycle 10 15600          144906.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       195510554.
##  4 cycle 3  15600       192054287.
##  5 cycle 4  15600       144946745.
##  6 cycle 5  15600        85769478.
##  7 cycle 6  15600        43258748.
##  8 cycle 7  15600        12525541.
##  9 cycle 8  15600         2759831.
## 10 cycle 9  15600          500002.
## 11 cycle 10 15600          160001.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       193139346.
##  4 cycle 3  15600       191003263.
##  5 cycle 4  15600       143697561.
##  6 cycle 5  15600        86361249.
##  7 cycle 6  15600        44483614.
##  8 cycle 7  15600        12581984.
##  9 cycle 8  15600         2807240.
## 10 cycle 9  15600          556488.
## 11 cycle 10 15600          144906.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       195746133.
##  4 cycle 3  15600       193308161.
##  5 cycle 4  15600       144510432.
##  6 cycle 5  15600        85589918.
##  7 cycle 6  15600        42390693.
##  8 cycle 7  15600        12101180.
##  9 cycle 8  15600         2878630.
## 10 cycle 9  15600          518831.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251790855.
##  3 cycle 2  15600       195406334.
##  4 cycle 3  15600       192031110.
##  5 cycle 4  15600       142809850.
##  6 cycle 5  15600        84431377.
##  7 cycle 6  15600        42646936.
##  8 cycle 7  15600        12400052.
##  9 cycle 8  15600         2694409.
## 10 cycle 9  15600          474898.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       193406651.
##  4 cycle 3  15600       191770740.
##  5 cycle 4  15600       143751998.
##  6 cycle 5  15600        85961372.
##  7 cycle 6  15600        43683093.
##  8 cycle 7  15600        13061840.
##  9 cycle 8  15600         2875908.
## 10 cycle 9  15600          575317.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       197270962.
##  4 cycle 3  15600       194215029.
##  5 cycle 4  15600       144488167.
##  6 cycle 5  15600        84347060.
##  7 cycle 6  15600        41838220.
##  8 cycle 7  15600        11991210.
##  9 cycle 8  15600         2700854.
## 10 cycle 9  15600          523015.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       194491538.
##  4 cycle 3  15600       191514144.
##  5 cycle 4  15600       143472341.
##  6 cycle 5  15600        84420403.
##  7 cycle 6  15600        42559038.
##  8 cycle 7  15600        12079516.
##  9 cycle 8  15600         2692897.
## 10 cycle 9  15600          550212.
## 11 cycle 10 15600          163020.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       195410538.
##  4 cycle 3  15600       192035452.
##  5 cycle 4  15600       144100858.
##  6 cycle 5  15600        85520701.
##  7 cycle 6  15600        43307664.
##  8 cycle 7  15600        12689294.
##  9 cycle 8  15600         2829233.
## 10 cycle 9  15600          598329.
## 11 cycle 10 15600          184152.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       193683127.
##  4 cycle 3  15600       190847796.
##  5 cycle 4  15600       143674164.
##  6 cycle 5  15600        84791506.
##  7 cycle 6  15600        42471139.
##  8 cycle 7  15600        12214890.
##  9 cycle 8  15600         2855793.
## 10 cycle 9  15600          518831.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       194334951.
##  4 cycle 3  15600       191739591.
##  5 cycle 4  15600       143920950.
##  6 cycle 5  15600        84769929.
##  7 cycle 6  15600        42531972.
##  8 cycle 7  15600        12263476.
##  9 cycle 8  15600         2915837.
## 10 cycle 9  15600          571132.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       193853984.
##  4 cycle 3  15600       191902872.
##  5 cycle 4  15600       144608205.
##  6 cycle 5  15600        85844524.
##  7 cycle 6  15600        43052168.
##  8 cycle 7  15600        12849501.
##  9 cycle 8  15600         2995662.
## 10 cycle 9  15600          541844.
## 11 cycle 10 15600          187171.
## 12 cycle 11 15600           50442.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       194543138.
##  4 cycle 3  15600       192521705.
##  5 cycle 4  15600       145166790.
##  6 cycle 5  15600        84682198.
##  7 cycle 6  15600        43060849.
##  8 cycle 7  15600        12413859.
##  9 cycle 8  15600         2903201.
## 10 cycle 9  15600          546028.
## 11 cycle 10 15600          135850.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       196481155.
##  4 cycle 3  15600       193146609.
##  5 cycle 4  15600       144077781.
##  6 cycle 5  15600        84928237.
##  7 cycle 6  15600        42641477.
##  8 cycle 7  15600        11946425.
##  9 cycle 8  15600         2609571.
## 10 cycle 9  15600          585777.
## 11 cycle 10 15600          181133.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       194449875.
##  4 cycle 3  15600       191996767.
##  5 cycle 4  15600       144779132.
##  6 cycle 5  15600        85468611.
##  7 cycle 6  15600        43431062.
##  8 cycle 7  15600        12685809.
##  9 cycle 8  15600         2846069.
## 10 cycle 9  15600          573225.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252389209.
##  3 cycle 2  15600       195405315.
##  4 cycle 3  15600       193798465.
##  5 cycle 4  15600       145265552.
##  6 cycle 5  15600        85690648.
##  7 cycle 6  15600        42982383.
##  8 cycle 7  15600        11811877.
##  9 cycle 8  15600         2619151.
## 10 cycle 9  15600          502094.
## 11 cycle 10 15600          135850.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251225742.
##  3 cycle 2  15600       194777060.
##  4 cycle 3  15600       192663115.
##  5 cycle 4  15600       145792633.
##  6 cycle 5  15600        85032742.
##  7 cycle 6  15600        42582872.
##  8 cycle 7  15600        12134817.
##  9 cycle 8  15600         2691719.
## 10 cycle 9  15600          539752.
## 11 cycle 10 15600          156982.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       193417479.
##  4 cycle 3  15600       190630466.
##  5 cycle 4  15600       144248038.
##  6 cycle 5  15600        85979516.
##  7 cycle 6  15600        42773813.
##  8 cycle 7  15600        12354248.
##  9 cycle 8  15600         2759498.
## 10 cycle 9  15600          527199.
## 11 cycle 10 15600          184152.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       195893672.
##  4 cycle 3  15600       192887400.
##  5 cycle 4  15600       145437816.
##  6 cycle 5  15600        85354844.
##  7 cycle 6  15600        42536445.
##  8 cycle 7  15600        12039164.
##  9 cycle 8  15600         2835423.
## 10 cycle 9  15600          573225.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       195848443.
##  4 cycle 3  15600       193198190.
##  5 cycle 4  15600       146263126.
##  6 cycle 5  15600        85590260.
##  7 cycle 6  15600        43497862.
##  8 cycle 7  15600        12484181.
##  9 cycle 8  15600         2800094.
## 10 cycle 9  15600          571132.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       194589005.
##  4 cycle 3  15600       190911691.
##  5 cycle 4  15600       143165654.
##  6 cycle 5  15600        85033427.
##  7 cycle 6  15600        42710501.
##  8 cycle 7  15600        11923049.
##  9 cycle 8  15600         2819286.
## 10 cycle 9  15600          502094.
## 11 cycle 10 15600          126793.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       195460991.
##  4 cycle 3  15600       191896945.
##  5 cycle 4  15600       142171713.
##  6 cycle 5  15600        84867912.
##  7 cycle 6  15600        42595784.
##  8 cycle 7  15600        12219384.
##  9 cycle 8  15600         2833211.
## 10 cycle 9  15600          604605.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       193797922.
##  4 cycle 3  15600       189537418.
##  5 cycle 4  15600       143201840.
##  6 cycle 5  15600        84399502.
##  7 cycle 6  15600        42078828.
##  8 cycle 7  15600        12292996.
##  9 cycle 8  15600         2732316.
## 10 cycle 9  15600          581593.
## 11 cycle 10 15600          160001.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       197032326.
##  4 cycle 3  15600       193459721.
##  5 cycle 4  15600       144145820.
##  6 cycle 5  15600        86376663.
##  7 cycle 6  15600        43164139.
##  8 cycle 7  15600        12298376.
##  9 cycle 8  15600         2881797.
## 10 cycle 9  15600          512555.
## 11 cycle 10 15600          153963.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       193964065.
##  4 cycle 3  15600       191209730.
##  5 cycle 4  15600       142952585.
##  6 cycle 5  15600        83952003.
##  7 cycle 6  15600        42240472.
##  8 cycle 7  15600        12433944.
##  9 cycle 8  15600         2866917.
## 10 cycle 9  15600          529291.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       196945943.
##  4 cycle 3  15600       193510431.
##  5 cycle 4  15600       143696398.
##  6 cycle 5  15600        84555062.
##  7 cycle 6  15600        42421971.
##  8 cycle 7  15600        12419372.
##  9 cycle 8  15600         2718168.
## 10 cycle 9  15600          525107.
## 11 cycle 10 15600          117736.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       194532182.
##  4 cycle 3  15600       191680474.
##  5 cycle 4  15600       143616908.
##  6 cycle 5  15600        84231249.
##  7 cycle 6  15600        42415517.
##  8 cycle 7  15600        12237186.
##  9 cycle 8  15600         2674961.
## 10 cycle 9  15600          529291.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252090032.
##  3 cycle 2  15600       194788783.
##  4 cycle 3  15600       192667893.
##  5 cycle 4  15600       143549537.
##  6 cycle 5  15600        85529611.
##  7 cycle 6  15600        42783750.
##  8 cycle 7  15600        12887061.
##  9 cycle 8  15600         2855204.
## 10 cycle 9  15600          546028.
## 11 cycle 10 15600          184152.
## 12 cycle 11 15600           56046.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       193934505.
##  4 cycle 3  15600       191383596.
##  5 cycle 4  15600       144486366.
##  6 cycle 5  15600        86192310.
##  7 cycle 6  15600        43586004.
##  8 cycle 7  15600        12310543.
##  9 cycle 8  15600         2973780.
## 10 cycle 9  15600          508371.
## 11 cycle 10 15600          144906.
## 12 cycle 11 15600           16814.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       193901889.
##  4 cycle 3  15600       191730893.
##  5 cycle 4  15600       144684877.
##  6 cycle 5  15600        85490557.
##  7 cycle 6  15600        43108531.
##  8 cycle 7  15600        12662251.
##  9 cycle 8  15600         2732650.
## 10 cycle 9  15600          562764.
## 11 cycle 10 15600          205284.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251807476.
##  3 cycle 2  15600       194519949.
##  4 cycle 3  15600       190147831.
##  5 cycle 4  15600       143653762.
##  6 cycle 5  15600        85085517.
##  7 cycle 6  15600        42578408.
##  8 cycle 7  15600        12554430.
##  9 cycle 8  15600         2709145.
## 10 cycle 9  15600          527199.
## 11 cycle 10 15600          196227.
## 12 cycle 11 15600           61651.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       194618946.
##  4 cycle 3  15600       191332451.
##  5 cycle 4  15600       142919219.
##  6 cycle 5  15600        84955300.
##  7 cycle 6  15600        43588740.
##  8 cycle 7  15600        12889028.
##  9 cycle 8  15600         2897345.
## 10 cycle 9  15600          537659.
## 11 cycle 10 15600          141887.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252422451.
##  3 cycle 2  15600       196773686.
##  4 cycle 3  15600       193816574.
##  5 cycle 4  15600       146257662.
##  6 cycle 5  15600        86847154.
##  7 cycle 6  15600        44080871.
##  8 cycle 7  15600        12510654.
##  9 cycle 8  15600         2809085.
## 10 cycle 9  15600          525107.
## 11 cycle 10 15600          141887.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       195357791.
##  4 cycle 3  15600       193288600.
##  5 cycle 4  15600       144305964.
##  6 cycle 5  15600        86254329.
##  7 cycle 6  15600        43721329.
##  8 cycle 7  15600        12278546.
##  9 cycle 8  15600         2833355.
## 10 cycle 9  15600          508371.
## 11 cycle 10 15600          141887.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252173137.
##  3 cycle 2  15600       194914663.
##  4 cycle 3  15600       192172520.
##  5 cycle 4  15600       145379715.
##  6 cycle 5  15600        86728243.
##  7 cycle 6  15600        43659006.
##  8 cycle 7  15600        12268989.
##  9 cycle 8  15600         2773167.
## 10 cycle 9  15600          577409.
## 11 cycle 10 15600          166038.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       194968301.
##  4 cycle 3  15600       191818413.
##  5 cycle 4  15600       142452852.
##  6 cycle 5  15600        84693838.
##  7 cycle 6  15600        42263802.
##  8 cycle 7  15600        11835314.
##  9 cycle 8  15600         2798327.
## 10 cycle 9  15600          541844.
## 11 cycle 10 15600          141887.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       194766995.
##  4 cycle 3  15600       190467463.
##  5 cycle 4  15600       141890923.
##  6 cycle 5  15600        83004508.
##  7 cycle 6  15600        41786572.
##  8 cycle 7  15600        12260622.
##  9 cycle 8  15600         2883898.
## 10 cycle 9  15600          556488.
## 11 cycle 10 15600          166038.
## 12 cycle 11 15600           44837.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600            2241.
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       192802223.
##  4 cycle 3  15600       191672937.
##  5 cycle 4  15600       144500493.
##  6 cycle 5  15600        85087238.
##  7 cycle 6  15600        43274392.
##  8 cycle 7  15600        12443064.
##  9 cycle 8  15600         2952710.
## 10 cycle 9  15600          564856.
## 11 cycle 10 15600          144906.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       195807289.
##  4 cycle 3  15600       192830026.
##  5 cycle 4  15600       143048497.
##  6 cycle 5  15600        84054436.
##  7 cycle 6  15600        42413285.
##  8 cycle 7  15600        12073688.
##  9 cycle 8  15600         2815085.
## 10 cycle 9  15600          566948.
## 11 cycle 10 15600          150944.
## 12 cycle 11 15600           22418.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       196351580.
##  4 cycle 3  15600       192726574.
##  5 cycle 4  15600       143702880.
##  6 cycle 5  15600        83712127.
##  7 cycle 6  15600        42473871.
##  8 cycle 7  15600        12311430.
##  9 cycle 8  15600         2759020.
## 10 cycle 9  15600          474898.
## 11 cycle 10 15600          126793.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       195936864.
##  4 cycle 3  15600       192611825.
##  5 cycle 4  15600       142809356.
##  6 cycle 5  15600        84392637.
##  7 cycle 6  15600        42031893.
##  8 cycle 7  15600        11917791.
##  9 cycle 8  15600         2598081.
## 10 cycle 9  15600          558580.
## 11 cycle 10 15600          163020.
## 12 cycle 11 15600           64453.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600            2241.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       194240797.
##  4 cycle 3  15600       191723647.
##  5 cycle 4  15600       146437077.
##  6 cycle 5  15600        87370744.
##  7 cycle 6  15600        44477903.
##  8 cycle 7  15600        12911518.
##  9 cycle 8  15600         2969547.
## 10 cycle 9  15600          537659.
## 11 cycle 10 15600          202265.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       195524824.
##  4 cycle 3  15600       192939997.
##  5 cycle 4  15600       143603306.
##  6 cycle 5  15600        85067364.
##  7 cycle 6  15600        42378033.
##  8 cycle 7  15600        12665542.
##  9 cycle 8  15600         2823599.
## 10 cycle 9  15600          533475.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       193959351.
##  4 cycle 3  15600       191770885.
##  5 cycle 4  15600       144861123.
##  6 cycle 5  15600        86434925.
##  7 cycle 6  15600        43334477.
##  8 cycle 7  15600        12288248.
##  9 cycle 8  15600         2751619.
## 10 cycle 9  15600          562764.
## 11 cycle 10 15600          160001.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       194132117.
##  4 cycle 3  15600       191409386.
##  5 cycle 4  15600       144910735.
##  6 cycle 5  15600        85980552.
##  7 cycle 6  15600        43567881.
##  8 cycle 7  15600        12342662.
##  9 cycle 8  15600         2867506.
## 10 cycle 9  15600          504187.
## 11 cycle 10 15600          166038.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       197491762.
##  4 cycle 3  15600       192709772.
##  5 cycle 4  15600       144266146.
##  6 cycle 5  15600        85625206.
##  7 cycle 6  15600        42829187.
##  8 cycle 7  15600        12548092.
##  9 cycle 8  15600         2594691.
## 10 cycle 9  15600          516739.
## 11 cycle 10 15600          144906.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251873959.
##  3 cycle 2  15600       193774862.
##  4 cycle 3  15600       191103956.
##  5 cycle 4  15600       142494213.
##  6 cycle 5  15600        85422367.
##  7 cycle 6  15600        43157942.
##  8 cycle 7  15600        12801547.
##  9 cycle 8  15600         2764144.
## 10 cycle 9  15600          550212.
## 11 cycle 10 15600          156982.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       194955688.
##  4 cycle 3  15600       190264758.
##  5 cycle 4  15600       142068013.
##  6 cycle 5  15600        84514639.
##  7 cycle 6  15600        42498200.
##  8 cycle 7  15600        12008441.
##  9 cycle 8  15600         2694854.
## 10 cycle 9  15600          558580.
## 11 cycle 10 15600          193208.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       196684499.
##  4 cycle 3  15600       193622725.
##  5 cycle 4  15600       145932518.
##  6 cycle 5  15600        86117598.
##  7 cycle 6  15600        42497205.
##  8 cycle 7  15600        12374964.
##  9 cycle 8  15600         2740639.
## 10 cycle 9  15600          537659.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       193757535.
##  4 cycle 3  15600       191033831.
##  5 cycle 4  15600       143961785.
##  6 cycle 5  15600        84462566.
##  7 cycle 6  15600        43130381.
##  8 cycle 7  15600        12450289.
##  9 cycle 8  15600         2718280.
## 10 cycle 9  15600          506279.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       195723073.
##  4 cycle 3  15600       192494172.
##  5 cycle 4  15600       144276929.
##  6 cycle 5  15600        85134860.
##  7 cycle 6  15600        43726792.
##  8 cycle 7  15600        12652172.
##  9 cycle 8  15600         2867140.
## 10 cycle 9  15600          510463.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252056790.
##  3 cycle 2  15600       195547503.
##  4 cycle 3  15600       192442737.
##  5 cycle 4  15600       144439398.
##  6 cycle 5  15600        85524467.
##  7 cycle 6  15600        43357322.
##  8 cycle 7  15600        12146730.
##  9 cycle 8  15600         2805695.
## 10 cycle 9  15600          543936.
## 11 cycle 10 15600          175095.
## 12 cycle 11 15600           47639.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       197363206.
##  4 cycle 3  15600       192510552.
##  5 cycle 4  15600       142542981.
##  6 cycle 5  15600        85550170.
##  7 cycle 6  15600        42995057.
##  8 cycle 7  15600        12672828.
##  9 cycle 8  15600         2892045.
## 10 cycle 9  15600          615066.
## 11 cycle 10 15600          214341.
## 12 cycle 11 15600           39232.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       193735237.
##  4 cycle 3  15600       191307677.
##  5 cycle 4  15600       141438363.
##  6 cycle 5  15600        84565351.
##  7 cycle 6  15600        42533214.
##  8 cycle 7  15600        12383076.
##  9 cycle 8  15600         2731138.
## 10 cycle 9  15600          531383.
## 11 cycle 10 15600          153963.
## 12 cycle 11 15600           22418.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252023548.
##  3 cycle 2  15600       195030095.
##  4 cycle 3  15600       193222818.
##  5 cycle 4  15600       144495842.
##  6 cycle 5  15600        85455602.
##  7 cycle 6  15600        43139567.
##  8 cycle 7  15600        12414624.
##  9 cycle 8  15600         2903647.
## 10 cycle 9  15600          569040.
## 11 cycle 10 15600          187171.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       195518962.
##  4 cycle 3  15600       191914909.
##  5 cycle 4  15600       143920281.
##  6 cycle 5  15600        86251599.
##  7 cycle 6  15600        43617543.
##  8 cycle 7  15600        12424946.
##  9 cycle 8  15600         2747307.
## 10 cycle 9  15600          516739.
## 11 cycle 10 15600          160001.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       250527662.
##  3 cycle 2  15600       195834682.
##  4 cycle 3  15600       192623559.
##  5 cycle 4  15600       144685196.
##  6 cycle 5  15600        85567305.
##  7 cycle 6  15600        43705195.
##  8 cycle 7  15600        12772088.
##  9 cycle 8  15600         2872774.
## 10 cycle 9  15600          525107.
## 11 cycle 10 15600          156982.
## 12 cycle 11 15600           11209.
## 13 cycle 12 15600               0 
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       196138680.
##  4 cycle 3  15600       191429963.
##  5 cycle 4  15600       143067912.
##  6 cycle 5  15600        84100013.
##  7 cycle 6  15600        42030403.
##  8 cycle 7  15600        11861471.
##  9 cycle 8  15600         2537703.
## 10 cycle 9  15600          514647.
## 11 cycle 10 15600          160001.
## 12 cycle 11 15600           25221.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       194093130.
##  4 cycle 3  15600       192559664.
##  5 cycle 4  15600       145077855.
##  6 cycle 5  15600        85815731.
##  7 cycle 6  15600        43153473.
##  8 cycle 7  15600        12341460.
##  9 cycle 8  15600         2767167.
## 10 cycle 9  15600          556488.
## 11 cycle 10 15600          147925.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251308847.
##  3 cycle 2  15600       195446212.
##  4 cycle 3  15600       192613567.
##  5 cycle 4  15600       143240001.
##  6 cycle 5  15600        84225086.
##  7 cycle 6  15600        42632287.
##  8 cycle 7  15600        12386561.
##  9 cycle 8  15600         2709845.
## 10 cycle 9  15600          495818.
## 11 cycle 10 15600          108680.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252239620.
##  3 cycle 2  15600       194538424.
##  4 cycle 3  15600       192390721.
##  5 cycle 4  15600       143900516.
##  6 cycle 5  15600        84436503.
##  7 cycle 6  15600        42887783.
##  8 cycle 7  15600        12020475.
##  9 cycle 8  15600         2594691.
## 10 cycle 9  15600          516739.
## 11 cycle 10 15600          120755.
## 12 cycle 11 15600            8407.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       195365690.
##  4 cycle 3  15600       193482753.
##  5 cycle 4  15600       145060096.
##  6 cycle 5  15600        84564684.
##  7 cycle 6  15600        42361395.
##  8 cycle 7  15600        12050445.
##  9 cycle 8  15600         2809308.
## 10 cycle 9  15600          550212.
## 11 cycle 10 15600          208303.
## 12 cycle 11 15600           14012.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       194750177.
##  4 cycle 3  15600       190615684.
##  5 cycle 4  15600       143387562.
##  6 cycle 5  15600        85072157.
##  7 cycle 6  15600        42985867.
##  8 cycle 7  15600        12216663.
##  9 cycle 8  15600         2812730.
## 10 cycle 9  15600          527199.
## 11 cycle 10 15600          163020.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       194949317.
##  4 cycle 3  15600       191786248.
##  5 cycle 4  15600       143889559.
##  6 cycle 5  15600        85465853.
##  7 cycle 6  15600        43203374.
##  8 cycle 7  15600        12562785.
##  9 cycle 8  15600         2753752.
## 10 cycle 9  15600          546028.
## 11 cycle 10 15600          199246.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       194260672.
##  4 cycle 3  15600       190515849.
##  5 cycle 4  15600       144087575.
##  6 cycle 5  15600        84372088.
##  7 cycle 6  15600        42411796.
##  8 cycle 7  15600        12292681.
##  9 cycle 8  15600         2718168.
## 10 cycle 9  15600          474898.
## 11 cycle 10 15600          147925.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251923822.
##  3 cycle 2  15600       194104343.
##  4 cycle 3  15600       191019484.
##  5 cycle 4  15600       142378536.
##  6 cycle 5  15600        84356331.
##  7 cycle 6  15600        42244441.
##  8 cycle 7  15600        12239213.
##  9 cycle 8  15600         2623607.
## 10 cycle 9  15600          500002.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           42035.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            4829.
## 15 cycle 14 15600               0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       194345145.
##  4 cycle 3  15600       190100593.
##  5 cycle 4  15600       144237955.
##  6 cycle 5  15600        86063832.
##  7 cycle 6  15600        44161079.
##  8 cycle 7  15600        12872622.
##  9 cycle 8  15600         2924606.
## 10 cycle 9  15600          527199.
## 11 cycle 10 15600          156982.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       197274785.
##  4 cycle 3  15600       193416971.
##  5 cycle 4  15600       145806584.
##  6 cycle 5  15600        86004201.
##  7 cycle 6  15600        42652652.
##  8 cycle 7  15600        12143560.
##  9 cycle 8  15600         2699565.
## 10 cycle 9  15600          506279.
## 11 cycle 10 15600          156982.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       195191777.
##  4 cycle 3  15600       191188863.
##  5 cycle 4  15600       144586959.
##  6 cycle 5  15600        86306438.
##  7 cycle 6  15600        43941577.
##  8 cycle 7  15600        12881426.
##  9 cycle 8  15600         2871373.
## 10 cycle 9  15600          518831.
## 11 cycle 10 15600          126793.
## 12 cycle 11 15600           19616.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       194601109.
##  4 cycle 3  15600       191032670.
##  5 cycle 4  15600       145556661.
##  6 cycle 5  15600        86529512.
##  7 cycle 6  15600        43547525.
##  8 cycle 7  15600        12985762.
##  9 cycle 8  15600         2969769.
## 10 cycle 9  15600          604605.
## 11 cycle 10 15600          202265.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       196204550.
##  4 cycle 3  15600       193535785.
##  5 cycle 4  15600       144503281.
##  6 cycle 5  15600        85296240.
##  7 cycle 6  15600        42611918.
##  8 cycle 7  15600        12215898.
##  9 cycle 8  15600         2700154.
## 10 cycle 9  15600          546028.
## 11 cycle 10 15600          153963.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       195666502.
##  4 cycle 3  15600       192922469.
##  5 cycle 4  15600       144355432.
##  6 cycle 5  15600        86458232.
##  7 cycle 6  15600        43082460.
##  8 cycle 7  15600        12514588.
##  9 cycle 8  15600         2759576.
## 10 cycle 9  15600          587869.
## 11 cycle 10 15600          163020.
## 12 cycle 11 15600           33628.
## 13 cycle 12 15600            5203.
## 14 cycle 13 15600            2415.
## 15 cycle 14 15600               0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251358710.
##  3 cycle 2  15600       193948775.
##  4 cycle 3  15600       190278088.
##  5 cycle 4  15600       144162241.
##  6 cycle 5  15600        85264041.
##  7 cycle 6  15600        43340193.
##  8 cycle 7  15600        12481327.
##  9 cycle 8  15600         2865406.
## 10 cycle 9  15600          529291.
## 11 cycle 10 15600          166038.
## 12 cycle 11 15600           53244.
## 13 cycle 12 15600           13006.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       195988464.
##  4 cycle 3  15600       193298024.
##  5 cycle 4  15600       144048890.
##  6 cycle 5  15600        84900804.
##  7 cycle 6  15600        43283325.
##  8 cycle 7  15600        12748457.
##  9 cycle 8  15600         2759099.
## 10 cycle 9  15600          575317.
## 11 cycle 10 15600          156982.
## 12 cycle 11 15600           28023.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       194120013.
##  4 cycle 3  15600       189871094.
##  5 cycle 4  15600       143441824.
##  6 cycle 5  15600        85109509.
##  7 cycle 6  15600        43447709.
##  8 cycle 7  15600        12937237.
##  9 cycle 8  15600         3019167.
## 10 cycle 9  15600          581593.
## 11 cycle 10 15600          163020.
## 12 cycle 11 15600           30825.
## 13 cycle 12 15600            7804.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       195408372.
##  4 cycle 3  15600       193365971.
##  5 cycle 4  15600       144807498.
##  6 cycle 5  15600        84435142.
##  7 cycle 6  15600        42500189.
##  8 cycle 7  15600        12002042.
##  9 cycle 8  15600         2692308.
## 10 cycle 9  15600          502094.
## 11 cycle 10 15600          120755.
## 12 cycle 11 15600           22418.
## 13 cycle 12 15600           10405.
## 14 cycle 13 15600            7244.
## 15 cycle 14 15600               0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252206378.
##  3 cycle 2  15600       196184038.
##  4 cycle 3  15600       193810925.
##  5 cycle 4  15600       145747990.
##  6 cycle 5  15600        86002480.
##  7 cycle 6  15600        43095120.
##  8 cycle 7  15600        12271017.
##  9 cycle 8  15600         2959489.
## 10 cycle 9  15600          529291.
## 11 cycle 10 15600          169057.
## 12 cycle 11 15600           36430.
## 13 cycle 12 15600            2601.
## 14 cycle 13 15600               0 
## 15 cycle 14 15600               0
# Females
discounted_costs_f_altA <-
  map(final_cost_f2_altA, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_f_altA
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       138961756.
##  4 cycle 3  10400       115436992.
##  5 cycle 4  10400       117042031.
##  6 cycle 5  10400        81824226.
##  7 cycle 6  10400        58442651.
##  8 cycle 7  10400        22679843.
##  9 cycle 8  10400         4310021.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       137011806.
##  4 cycle 3  10400       114762206.
##  5 cycle 4  10400       114906555.
##  6 cycle 5  10400        80532806.
##  7 cycle 6  10400        56860982.
##  8 cycle 7  10400        22777889.
##  9 cycle 8  10400         4808821.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       138872115.
##  4 cycle 3  10400       116028976.
##  5 cycle 4  10400       115786706.
##  6 cycle 5  10400        81542699.
##  7 cycle 6  10400        57098436.
##  8 cycle 7  10400        22653218.
##  9 cycle 8  10400         4677076.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       138690621.
##  4 cycle 3  10400       116215325.
##  5 cycle 4  10400       116161374.
##  6 cycle 5  10400        81601745.
##  7 cycle 6  10400        58225255.
##  8 cycle 7  10400        22667942.
##  9 cycle 8  10400         4479699.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           61099.
## 12 cycle 11 10400               0 
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       138229456.
##  4 cycle 3  10400       115294682.
##  5 cycle 4  10400       114999895.
##  6 cycle 5  10400        81134465.
##  7 cycle 6  10400        56849923.
##  8 cycle 7  10400        22621582.
##  9 cycle 8  10400         4836016.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       138988790.
##  4 cycle 3  10400       115241000.
##  5 cycle 4  10400       116981026.
##  6 cycle 5  10400        82203871.
##  7 cycle 6  10400        58315016.
##  8 cycle 7  10400        23177578.
##  9 cycle 8  10400         4509892.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       138548019.
##  4 cycle 3  10400       114352566.
##  5 cycle 4  10400       115099092.
##  6 cycle 5  10400        81216995.
##  7 cycle 6  10400        57053833.
##  8 cycle 7  10400        22805139.
##  9 cycle 8  10400         4919660.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220636028.
##  3 cycle 2  10400       139847880.
##  4 cycle 3  10400       115346182.
##  5 cycle 4  10400       116126258.
##  6 cycle 5  10400        81607093.
##  7 cycle 6  10400        58578151.
##  8 cycle 7  10400        23205769.
##  9 cycle 8  10400         4905119.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       138416167.
##  4 cycle 3  10400       115001330.
##  5 cycle 4  10400       115913288.
##  6 cycle 5  10400        81419950.
##  7 cycle 6  10400        57510741.
##  8 cycle 7  10400        22749069.
##  9 cycle 8  10400         4615884.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221411398.
##  3 cycle 2  10400       139648523.
##  4 cycle 3  10400       116339619.
##  5 cycle 4  10400       117298649.
##  6 cycle 5  10400        82461914.
##  7 cycle 6  10400        58869486.
##  8 cycle 7  10400        23716970.
##  9 cycle 8  10400         4712049.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       138774728.
##  4 cycle 3  10400       116630423.
##  5 cycle 4  10400       116497640.
##  6 cycle 5  10400        81400657.
##  7 cycle 6  10400        57735356.
##  8 cycle 7  10400        22810777.
##  9 cycle 8  10400         4382187.
## 10 cycle 9  10400          234802.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       138365418.
##  4 cycle 3  10400       115405873.
##  5 cycle 4  10400       116511038.
##  6 cycle 5  10400        81169336.
##  7 cycle 6  10400        57642124.
##  8 cycle 7  10400        22986501.
##  9 cycle 8  10400         4736048.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       138671017.
##  4 cycle 3  10400       116476467.
##  5 cycle 4  10400       116179027.
##  6 cycle 5  10400        80973363.
##  7 cycle 6  10400        56915047.
##  8 cycle 7  10400        22356898.
##  9 cycle 8  10400         4767284.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220463723.
##  3 cycle 2  10400       138189458.
##  4 cycle 3  10400       114632453.
##  5 cycle 4  10400       115765978.
##  6 cycle 5  10400        80662537.
##  7 cycle 6  10400        58266664.
##  8 cycle 7  10400        22542960.
##  9 cycle 8  10400         4968701.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       139094080.
##  4 cycle 3  10400       115870652.
##  5 cycle 4  10400       116341209.
##  6 cycle 5  10400        80694617.
##  7 cycle 6  10400        57781741.
##  8 cycle 7  10400        22980550.
##  9 cycle 8  10400         4807171.
## 10 cycle 9  10400          257897.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       139126648.
##  4 cycle 3  10400       115430622.
##  5 cycle 4  10400       115383179.
##  6 cycle 5  10400        81057750.
##  7 cycle 6  10400        57265699.
##  8 cycle 7  10400        23384939.
##  9 cycle 8  10400         4880514.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       138182817.
##  4 cycle 3  10400       115292134.
##  5 cycle 4  10400       115797323.
##  6 cycle 5  10400        81478996.
##  7 cycle 6  10400        58675037.
##  8 cycle 7  10400        23137485.
##  9 cycle 8  10400         5084928.
## 10 cycle 9  10400          346429.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       137787106.
##  4 cycle 3  10400       114407889.
##  5 cycle 4  10400       115984510.
##  6 cycle 5  10400        81161664.
##  7 cycle 6  10400        56476415.
##  8 cycle 7  10400        22651965.
##  9 cycle 8  10400         4579061.
## 10 cycle 9  10400          211706.
## 11 cycle 10 10400           66654.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220722180.
##  3 cycle 2  10400       140405798.
##  4 cycle 3  10400       115208968.
##  5 cycle 4  10400       115544076.
##  6 cycle 5  10400        80333575.
##  7 cycle 6  10400        57405223.
##  8 cycle 7  10400        22948914.
##  9 cycle 8  10400         4538804.
## 10 cycle 9  10400          223254.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       138174598.
##  4 cycle 3  10400       114865390.
##  5 cycle 4  10400       115368012.
##  6 cycle 5  10400        80611387.
##  7 cycle 6  10400        57565942.
##  8 cycle 7  10400        22740924.
##  9 cycle 8  10400         4553345.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220743718.
##  3 cycle 2  10400       137956109.
##  4 cycle 3  10400       113790245.
##  5 cycle 4  10400       115532279.
##  6 cycle 5  10400        80645792.
##  7 cycle 6  10400        58221415.
##  8 cycle 7  10400        23259957.
##  9 cycle 8  10400         5002394.
## 10 cycle 9  10400          223254.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       138433558.
##  4 cycle 3  10400       115494497.
##  5 cycle 4  10400       116290125.
##  6 cycle 5  10400        80625566.
##  7 cycle 6  10400        57428938.
##  8 cycle 7  10400        22979297.
##  9 cycle 8  10400         4678726.
## 10 cycle 9  10400          207857.
## 11 cycle 10 10400           55545.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       137490834.
##  4 cycle 3  10400       114533091.
##  5 cycle 4  10400       114034429.
##  6 cycle 5  10400        78869880.
##  7 cycle 6  10400        55406948.
##  8 cycle 7  10400        22133246.
##  9 cycle 8  10400         4412313.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       138636552.
##  4 cycle 3  10400       116527422.
##  5 cycle 4  10400       116957412.
##  6 cycle 5  10400        81782149.
##  7 cycle 6  10400        57804227.
##  8 cycle 7  10400        22888772.
##  9 cycle 8  10400         4447116.
## 10 cycle 9  10400          219405.
## 11 cycle 10 10400           66654.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       137513758.
##  4 cycle 3  10400       114598969.
##  5 cycle 4  10400       114544000.
##  6 cycle 5  10400        79345299.
##  7 cycle 6  10400        57410291.
##  8 cycle 7  10400        23088930.
##  9 cycle 8  10400         4491613.
## 10 cycle 9  10400          215556.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       137640077.
##  4 cycle 3  10400       113790973.
##  5 cycle 4  10400       115559264.
##  6 cycle 5  10400        80903378.
##  7 cycle 6  10400        57779590.
##  8 cycle 7  10400        22937638.
##  9 cycle 8  10400         4970721.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       137618576.
##  4 cycle 3  10400       115210971.
##  5 cycle 4  10400       115266035.
##  6 cycle 5  10400        80263133.
##  7 cycle 6  10400        57923231.
##  8 cycle 7  10400        22663869.
##  9 cycle 8  10400         4966548.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       139208541.
##  4 cycle 3  10400       115208423.
##  5 cycle 4  10400       116191624.
##  6 cycle 5  10400        80746924.
##  7 cycle 6  10400        57276481.
##  8 cycle 7  10400        23159096.
##  9 cycle 8  10400         4548868.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       138272775.
##  4 cycle 3  10400       114674673.
##  5 cycle 4  10400       115637522.
##  6 cycle 5  10400        80482132.
##  7 cycle 6  10400        57639299.
##  8 cycle 7  10400        22450556.
##  9 cycle 8  10400         4581451.
## 10 cycle 9  10400          227103.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       139312569.
##  4 cycle 3  10400       116359455.
##  5 cycle 4  10400       117067225.
##  6 cycle 5  10400        82205728.
##  7 cycle 6  10400        58396266.
##  8 cycle 7  10400        22527609.
##  9 cycle 8  10400         4817671.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400           72208.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220528337.
##  3 cycle 2  10400       137964963.
##  4 cycle 3  10400       114620624.
##  5 cycle 4  10400       114423274.
##  6 cycle 5  10400        79722386.
##  7 cycle 6  10400        56938853.
##  8 cycle 7  10400        22217506.
##  9 cycle 8  10400         4561759.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       139023255.
##  4 cycle 3  10400       116099767.
##  5 cycle 4  10400       115079649.
##  6 cycle 5  10400        80165949.
##  7 cycle 6  10400        56927149.
##  8 cycle 7  10400        22904434.
##  9 cycle 8  10400         4765937.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       138581377.
##  4 cycle 3  10400       114708885.
##  5 cycle 4  10400       116330107.
##  6 cycle 5  10400        81773777.
##  7 cycle 6  10400        57777901.
##  8 cycle 7  10400        23330437.
##  9 cycle 8  10400         4925181.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220851408.
##  3 cycle 2  10400       138106458.
##  4 cycle 3  10400       115773292.
##  5 cycle 4  10400       116002563.
##  6 cycle 5  10400        81532702.
##  7 cycle 6  10400        58169870.
##  8 cycle 7  10400        22880001.
##  9 cycle 8  10400         4382254.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       138626592.
##  4 cycle 3  10400       115339812.
##  5 cycle 4  10400       114492010.
##  6 cycle 5  10400        80112269.
##  7 cycle 6  10400        55867021.
##  8 cycle 7  10400        22104431.
##  9 cycle 8  10400         4396965.
## 10 cycle 9  10400          230952.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       137849395.
##  4 cycle 3  10400       114767119.
##  5 cycle 4  10400       115301551.
##  6 cycle 5  10400        79700760.
##  7 cycle 6  10400        56796043.
##  8 cycle 7  10400        21986653.
##  9 cycle 8  10400         4338667.
## 10 cycle 9  10400          204008.
## 11 cycle 10 10400           72208.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       138895829.
##  4 cycle 3  10400       115228259.
##  5 cycle 4  10400       116096598.
##  6 cycle 5  10400        81807257.
##  7 cycle 6  10400        58004297.
##  8 cycle 7  10400        23252128.
##  9 cycle 8  10400         4709659.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       139425606.
##  4 cycle 3  10400       116220055.
##  5 cycle 4  10400       116466000.
##  6 cycle 5  10400        82156912.
##  7 cycle 6  10400        57642678.
##  8 cycle 7  10400        22450242.
##  9 cycle 8  10400         4578084.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          177743.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       139429716.
##  4 cycle 3  10400       116279017.
##  5 cycle 4  10400       114916076.
##  6 cycle 5  10400        79591265.
##  7 cycle 6  10400        56860889.
##  8 cycle 7  10400        22503806.
##  9 cycle 8  10400         4507131.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       140206441.
##  4 cycle 3  10400       116648439.
##  5 cycle 4  10400       116664393.
##  6 cycle 5  10400        81938612.
##  7 cycle 6  10400        57681660.
##  8 cycle 7  10400        22796994.
##  9 cycle 8  10400         4484679.
## 10 cycle 9  10400          188611.
## 11 cycle 10 10400           33327.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       139272254.
##  4 cycle 3  10400       114826081.
##  5 cycle 4  10400       115743459.
##  6 cycle 5  10400        80796440.
##  7 cycle 6  10400        57587016.
##  8 cycle 7  10400        22027686.
##  9 cycle 8  10400         4645337.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       139038432.
##  4 cycle 3  10400       115148370.
##  5 cycle 4  10400       116061988.
##  6 cycle 5  10400        81669863.
##  7 cycle 6  10400        58263009.
##  8 cycle 7  10400        23089245.
##  9 cycle 8  10400         4848841.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220399109.
##  3 cycle 2  10400       136220221.
##  4 cycle 3  10400       113746934.
##  5 cycle 4  10400       114716398.
##  6 cycle 5  10400        79778641.
##  7 cycle 6  10400        56829126.
##  8 cycle 7  10400        22612811.
##  9 cycle 8  10400         4696768.
## 10 cycle 9  10400          223254.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221454474.
##  3 cycle 2  10400       138826900.
##  4 cycle 3  10400       116128338.
##  5 cycle 4  10400       115877077.
##  6 cycle 5  10400        80798764.
##  7 cycle 6  10400        57269447.
##  8 cycle 7  10400        22716494.
##  9 cycle 8  10400         4478656.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       138311349.
##  4 cycle 3  10400       113828097.
##  5 cycle 4  10400       114936215.
##  6 cycle 5  10400        80075056.
##  7 cycle 6  10400        57278355.
##  8 cycle 7  10400        23048837.
##  9 cycle 8  10400         5013065.
## 10 cycle 9  10400          357976.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       138699158.
##  4 cycle 3  10400       115762738.
##  5 cycle 4  10400       115090855.
##  6 cycle 5  10400        80404251.
##  7 cycle 6  10400        56950096.
##  8 cycle 7  10400        22263239.
##  9 cycle 8  10400         4578017.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       137398980.
##  4 cycle 3  10400       114867026.
##  5 cycle 4  10400       115700612.
##  6 cycle 5  10400        80292422.
##  7 cycle 6  10400        56972306.
##  8 cycle 7  10400        22612498.
##  9 cycle 8  10400         4383164.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       138681767.
##  4 cycle 3  10400       115168751.
##  5 cycle 4  10400       115908422.
##  6 cycle 5  10400        81505270.
##  7 cycle 6  10400        57558816.
##  8 cycle 7  10400        22683603.
##  9 cycle 8  10400         4673509.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       139365214.
##  4 cycle 3  10400       115096686.
##  5 cycle 4  10400       115538030.
##  6 cycle 5  10400        81737748.
##  7 cycle 6  10400        57870855.
##  8 cycle 7  10400        23143748.
##  9 cycle 8  10400         4375927.
## 10 cycle 9  10400          196310.
## 11 cycle 10 10400           55545.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       137838644.
##  4 cycle 3  10400       114114899.
##  5 cycle 4  10400       115510456.
##  6 cycle 5  10400        81008225.
##  7 cycle 6  10400        57948634.
##  8 cycle 7  10400        23072017.
##  9 cycle 8  10400         4730764.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       138343917.
##  4 cycle 3  10400       114662481.
##  5 cycle 4  10400       115268225.
##  6 cycle 5  10400        79682858.
##  7 cycle 6  10400        56949543.
##  8 cycle 7  10400        22811715.
##  9 cycle 8  10400         4681856.
## 10 cycle 9  10400          234802.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       139285535.
##  4 cycle 3  10400       114415350.
##  5 cycle 4  10400       115152366.
##  6 cycle 5  10400        80585804.
##  7 cycle 6  10400        57650848.
##  8 cycle 7  10400        23166298.
##  9 cycle 8  10400         4606323.
## 10 cycle 9  10400          238651.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220765256.
##  3 cycle 2  10400       138648092.
##  4 cycle 3  10400       116561997.
##  5 cycle 4  10400       115891359.
##  6 cycle 5  10400        81011258.
##  7 cycle 6  10400        57838417.
##  8 cycle 7  10400        22836151.
##  9 cycle 8  10400         4722720.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220420647.
##  3 cycle 2  10400       137800542.
##  4 cycle 3  10400       115701590.
##  5 cycle 4  10400       115694356.
##  6 cycle 5  10400        81252799.
##  7 cycle 6  10400        58760312.
##  8 cycle 7  10400        23166927.
##  9 cycle 8  10400         4939182.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       140210550.
##  4 cycle 3  10400       116532881.
##  5 cycle 4  10400       116566776.
##  6 cycle 5  10400        81288370.
##  7 cycle 6  10400        57462851.
##  8 cycle 7  10400        22470916.
##  9 cycle 8  10400         4592559.
## 10 cycle 9  10400          219405.
## 11 cycle 10 10400           66654.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       138146457.
##  4 cycle 3  10400       115660465.
##  5 cycle 4  10400       117142408.
##  6 cycle 5  10400        82900847.
##  7 cycle 6  10400        58341065.
##  8 cycle 7  10400        23572569.
##  9 cycle 8  10400         5187050.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       138368421.
##  4 cycle 3  10400       115277211.
##  5 cycle 4  10400       115632761.
##  6 cycle 5  10400        80698799.
##  7 cycle 6  10400        56779362.
##  8 cycle 7  10400        22724951.
##  9 cycle 8  10400         4987653.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221368322.
##  3 cycle 2  10400       138093812.
##  4 cycle 3  10400       114956016.
##  5 cycle 4  10400       116363833.
##  6 cycle 5  10400        81628711.
##  7 cycle 6  10400        58148797.
##  8 cycle 7  10400        23678755.
##  9 cycle 8  10400         4903336.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       138360519.
##  4 cycle 3  10400       115213519.
##  5 cycle 4  10400       116127543.
##  6 cycle 5  10400        80955227.
##  7 cycle 6  10400        56623712.
##  8 cycle 7  10400        22432387.
##  9 cycle 8  10400         4774521.
## 10 cycle 9  10400          257897.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       139126648.
##  4 cycle 3  10400       115037724.
##  5 cycle 4  10400       116149662.
##  6 cycle 5  10400        80679972.
##  7 cycle 6  10400        57321546.
##  8 cycle 7  10400        22559248.
##  9 cycle 8  10400         4386968.
## 10 cycle 9  10400          257897.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       138249850.
##  4 cycle 3  10400       115235720.
##  5 cycle 4  10400       115454801.
##  6 cycle 5  10400        81627562.
##  7 cycle 6  10400        57938314.
##  8 cycle 7  10400        22985878.
##  9 cycle 8  10400         4878293.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       138048280.
##  4 cycle 3  10400       115218432.
##  5 cycle 4  10400       117069606.
##  6 cycle 5  10400        82218049.
##  7 cycle 6  10400        57895031.
##  8 cycle 7  10400        22755333.
##  9 cycle 8  10400         4433685.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221540627.
##  3 cycle 2  10400       137437082.
##  4 cycle 3  10400       114800786.
##  5 cycle 4  10400       116115747.
##  6 cycle 5  10400        81652885.
##  7 cycle 6  10400        58585369.
##  8 cycle 7  10400        23566615.
##  9 cycle 8  10400         5211789.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       137891607.
##  4 cycle 3  10400       114027912.
##  5 cycle 4  10400       115635836.
##  6 cycle 5  10400        80918956.
##  7 cycle 6  10400        58182710.
##  8 cycle 7  10400        23164734.
##  9 cycle 8  10400         4782565.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       139106254.
##  4 cycle 3  10400       115779480.
##  5 cycle 4  10400       116122488.
##  6 cycle 5  10400        80855719.
##  7 cycle 6  10400        58504027.
##  8 cycle 7  10400        23310702.
##  9 cycle 8  10400         5109297.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       137065557.
##  4 cycle 3  10400       113766224.
##  5 cycle 4  10400       116300553.
##  6 cycle 5  10400        81367634.
##  7 cycle 6  10400        57880992.
##  8 cycle 7  10400        23532158.
##  9 cycle 8  10400         5053085.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       138149460.
##  4 cycle 3  10400       114958927.
##  5 cycle 4  10400       115836800.
##  6 cycle 5  10400        80287550.
##  7 cycle 6  10400        56467692.
##  8 cycle 7  10400        22585247.
##  9 cycle 8  10400         4534630.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       137909787.
##  4 cycle 3  10400       114756564.
##  5 cycle 4  10400       115487831.
##  6 cycle 5  10400        80470978.
##  7 cycle 6  10400        56673937.
##  8 cycle 7  10400        22683604.
##  9 cycle 8  10400         4647727.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       139026258.
##  4 cycle 3  10400       115476480.
##  5 cycle 4  10400       116342999.
##  6 cycle 5  10400        81246285.
##  7 cycle 6  10400        57792984.
##  8 cycle 7  10400        22847739.
##  9 cycle 8  10400         4762370.
## 10 cycle 9  10400          227103.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400            5156.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       137981247.
##  4 cycle 3  10400       115077396.
##  5 cycle 4  10400       115364831.
##  6 cycle 5  10400        80206178.
##  7 cycle 6  10400        57057304.
##  8 cycle 7  10400        22565825.
##  9 cycle 8  10400         4591819.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       138044170.
##  4 cycle 3  10400       114429182.
##  5 cycle 4  10400       115625725.
##  6 cycle 5  10400        79850708.
##  7 cycle 6  10400        58219726.
##  8 cycle 7  10400        23315402.
##  9 cycle 8  10400         5087688.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221389860.
##  3 cycle 2  10400       137513441.
##  4 cycle 3  10400       114223904.
##  5 cycle 4  10400       114897623.
##  6 cycle 5  10400        80622784.
##  7 cycle 6  10400        57006035.
##  8 cycle 7  10400        22630354.
##  9 cycle 8  10400         4720330.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       137694618.
##  4 cycle 3  10400       114717254.
##  5 cycle 4  10400       114431005.
##  6 cycle 5  10400        79500371.
##  7 cycle 6  10400        56681432.
##  8 cycle 7  10400        22939205.
##  9 cycle 8  10400         4897008.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       138890296.
##  4 cycle 3  10400       115709418.
##  5 cycle 4  10400       115397967.
##  6 cycle 5  10400        80393321.
##  7 cycle 6  10400        57217532.
##  8 cycle 7  10400        23082979.
##  9 cycle 8  10400         4577647.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       138097921.
##  4 cycle 3  10400       114783861.
##  5 cycle 4  10400       116183893.
##  6 cycle 5  10400        80706929.
##  7 cycle 6  10400        58230600.
##  8 cycle 7  10400        22950167.
##  9 cycle 8  10400         4711309.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       137770505.
##  4 cycle 3  10400       115380395.
##  5 cycle 4  10400       115480796.
##  6 cycle 5  10400        80580231.
##  7 cycle 6  10400        56750701.
##  8 cycle 7  10400        22228155.
##  9 cycle 8  10400         4472832.
## 10 cycle 9  10400          180913.
## 11 cycle 10 10400           72208.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       138000534.
##  4 cycle 3  10400       114061034.
##  5 cycle 4  10400       116280014.
##  6 cycle 5  10400        80759936.
##  7 cycle 6  10400        57903939.
##  8 cycle 7  10400        23044765.
##  9 cycle 8  10400         4698551.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221196018.
##  3 cycle 2  10400       138647303.
##  4 cycle 3  10400       115153283.
##  5 cycle 4  10400       115911792.
##  6 cycle 5  10400        81406705.
##  7 cycle 6  10400        58144005.
##  8 cycle 7  10400        22558622.
##  9 cycle 8  10400         4709896.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       138768404.
##  4 cycle 3  10400       115097415.
##  5 cycle 4  10400       116041744.
##  6 cycle 5  10400        81127493.
##  7 cycle 6  10400        58437214.
##  8 cycle 7  10400        22899737.
##  9 cycle 8  10400         4643923.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400           66654.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       138710226.
##  4 cycle 3  10400       115127989.
##  5 cycle 4  10400       116389427.
##  6 cycle 5  10400        81569431.
##  7 cycle 6  10400        57984053.
##  8 cycle 7  10400        22784465.
##  9 cycle 8  10400         4604473.
## 10 cycle 9  10400          234802.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       137574940.
##  4 cycle 3  10400       113931098.
##  5 cycle 4  10400       115423561.
##  6 cycle 5  10400        80646725.
##  7 cycle 6  10400        57640527.
##  8 cycle 7  10400        23118376.
##  9 cycle 8  10400         5015522.
## 10 cycle 9  10400          357976.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       138247637.
##  4 cycle 3  10400       115114703.
##  5 cycle 4  10400       115583363.
##  6 cycle 5  10400        80366356.
##  7 cycle 6  10400        57059085.
##  8 cycle 7  10400        22980864.
##  9 cycle 8  10400         4735004.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       138390556.
##  4 cycle 3  10400       114786592.
##  5 cycle 4  10400       116128049.
##  6 cycle 5  10400        81406462.
##  7 cycle 6  10400        57521615.
##  8 cycle 7  10400        23236465.
##  9 cycle 8  10400         4575931.
## 10 cycle 9  10400          204008.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       139113684.
##  4 cycle 3  10400       115483213.
##  5 cycle 4  10400       114937689.
##  6 cycle 5  10400        79883964.
##  7 cycle 6  10400        57164113.
##  8 cycle 7  10400        22305214.
##  9 cycle 8  10400         4746349.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       139132499.
##  4 cycle 3  10400       115091411.
##  5 cycle 4  10400       116730559.
##  6 cycle 5  10400        81592682.
##  7 cycle 6  10400        57987893.
##  8 cycle 7  10400        22979926.
##  9 cycle 8  10400         4891555.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       138235780.
##  4 cycle 3  10400       115230807.
##  5 cycle 4  10400       115390826.
##  6 cycle 5  10400        80579065.
##  7 cycle 6  10400        58033725.
##  8 cycle 7  10400        23363012.
##  9 cycle 8  10400         4810368.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       139098507.
##  4 cycle 3  10400       115071937.
##  5 cycle 4  10400       115491897.
##  6 cycle 5  10400        80617436.
##  7 cycle 6  10400        56714423.
##  8 cycle 7  10400        22334971.
##  9 cycle 8  10400         4507131.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400           66654.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       140387935.
##  4 cycle 3  10400       116289210.
##  5 cycle 4  10400       116255410.
##  6 cycle 5  10400        80545827.
##  7 cycle 6  10400        56639931.
##  8 cycle 7  10400        22890965.
##  9 cycle 8  10400         4851601.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220506799.
##  3 cycle 2  10400       139311462.
##  4 cycle 3  10400       115295227.
##  5 cycle 4  10400       115745945.
##  6 cycle 5  10400        80793416.
##  7 cycle 6  10400        57622250.
##  8 cycle 7  10400        23108977.
##  9 cycle 8  10400         4663445.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       137592014.
##  4 cycle 3  10400       114045199.
##  5 cycle 4  10400       115522652.
##  6 cycle 5  10400        79661016.
##  7 cycle 6  10400        56281783.
##  8 cycle 7  10400        22455567.
##  9 cycle 8  10400         4793369.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       137747108.
##  4 cycle 3  10400       114628268.
##  5 cycle 4  10400       116430294.
##  6 cycle 5  10400        82016718.
##  7 cycle 6  10400        58163418.
##  8 cycle 7  10400        22960190.
##  9 cycle 8  10400         4337823.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       138446839.
##  4 cycle 3  10400       115101966.
##  5 cycle 4  10400       116656957.
##  6 cycle 5  10400        80892682.
##  7 cycle 6  10400        57978247.
##  8 cycle 7  10400        22963009.
##  9 cycle 8  10400         4532610.
## 10 cycle 9  10400          238651.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       138589124.
##  4 cycle 3  10400       114986953.
##  5 cycle 4  10400       116282984.
##  6 cycle 5  10400        81818654.
##  7 cycle 6  10400        58196012.
##  8 cycle 7  10400        22973975.
##  9 cycle 8  10400         4907509.
## 10 cycle 9  10400          342579.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       138355775.
##  4 cycle 3  10400       115577662.
##  5 cycle 4  10400       115627305.
##  6 cycle 5  10400        80843865.
##  7 cycle 6  10400        56941188.
##  8 cycle 7  10400        22132934.
##  9 cycle 8  10400         4647053.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220657566.
##  3 cycle 2  10400       138294276.
##  4 cycle 3  10400       114457387.
##  5 cycle 4  10400       114895643.
##  6 cycle 5  10400        79861871.
##  7 cycle 6  10400        57014759.
##  8 cycle 7  10400        22476553.
##  9 cycle 8  10400         4465158.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       139765987.
##  4 cycle 3  10400       115418431.
##  5 cycle 4  10400       116302238.
##  6 cycle 5  10400        81221401.
##  7 cycle 6  10400        58637191.
##  8 cycle 7  10400        22973031.
##  9 cycle 8  10400         4667012.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       137670432.
##  4 cycle 3  10400       116166373.
##  5 cycle 4  10400       116127354.
##  6 cycle 5  10400        81799118.
##  7 cycle 6  10400        59223701.
##  8 cycle 7  10400        23762074.
##  9 cycle 8  10400         4796196.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            8248.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       138232459.
##  4 cycle 3  10400       114882494.
##  5 cycle 4  10400       115541401.
##  6 cycle 5  10400        80188275.
##  7 cycle 6  10400        56677315.
##  8 cycle 7  10400        22492529.
##  9 cycle 8  10400         4558866.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       138792119.
##  4 cycle 3  10400       115594404.
##  5 cycle 4  10400       115526023.
##  6 cycle 5  10400        80276620.
##  7 cycle 6  10400        56695777.
##  8 cycle 7  10400        22314610.
##  9 cycle 8  10400         4914376.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       138640979.
##  4 cycle 3  10400       115518338.
##  5 cycle 4  10400       116407080.
##  6 cycle 5  10400        81025661.
##  7 cycle 6  10400        57996524.
##  8 cycle 7  10400        23324170.
##  9 cycle 8  10400         4779131.
## 10 cycle 9  10400          207857.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0

The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:

#Males
tot_discounted_costs_m_altA <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m_altA[[i]]$discounted_costs) 
tot_discounted_costs_m_altA[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m_altA)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1369744774
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1369586841
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1364367758
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1368070122
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1363900510
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1370235752
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1380537036
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1364306500
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1368210026
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1370136412
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1372975419
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1366926424
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1371119267
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1371797204
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1369393931
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1366735684
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1362143931
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1371691010
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1376873263
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1365593386
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1365188684
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1368810687
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1371035501
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1371486935
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1369789425
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1367836203
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1369756888
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1365741591
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1367872085
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1370585709
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1364320398
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1369285810
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1363702327
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1365871850
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1368717236
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1368816086
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1369022306
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1369017632
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1371504581
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1368504475
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1365625170
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1370567821
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1373226168
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1364266731
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1365281098
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1361178921
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1372530013
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1362820822
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1370059459
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1364364340
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1368811712
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1367930714
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1367760962
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1364720598
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1366450547
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1377094975
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1371363628
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1371722917
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1364168812
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1360112773
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1365907634
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1366843574
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1367647650
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1365908416
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1373486327
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1368304001
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1368727153
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1368360673
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1371236364
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1365004810
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1362382968
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1373518945
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1364816824
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1370731363
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1369960926
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1370154655
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1361797939
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1370346781
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1369397105
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1370147755
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1364478204
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1369258437
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1366046510
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1366525648
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1369226076
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1365041308
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1367664159
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1364093479
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1362476901
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1367932916
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1373326418
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1370326348
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1370875350
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1370834563
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1370842583
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1365326358
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1370601628
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1365502993
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1368381703
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1373880813
#Females
tot_discounted_costs_f_altA <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f_altA[[i]]$discounted_costs) 
tot_discounted_costs_f_altA[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f_altA)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1021552533
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1014453213
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1019361167
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1020283407
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1016639109
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1021913712
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1016947476
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1021976058
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1018269523
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1026133734
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1020818527
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1019373336
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1019220246
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1017253806
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1020210612
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1019256352
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1020071029
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1015873978
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1018761998
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1016849142
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1016785689
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1018177968
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1009506856
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1021522485
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1013567213
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1016215052
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1016364064
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1019265426
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1016080695
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1023552158
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1012658899
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1017900646
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1020432997
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1019419246
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1013489476
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1013475261
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1020723162
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1021819466
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1016690989
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1023004137
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1017721166
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1020675971
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1010641586
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1019333639
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1015172808
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1016159350
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1014672829
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1019031400
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1019530818
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1016801491
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1015111944
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1017704689
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1019970481
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1019464827
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1021662599
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1023528553
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1017076429
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1020829688
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1016795381
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1017673304
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1019028354
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1020591992
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1020692639
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1017149085
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1021930129
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1017728946
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1015315081
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1014988084
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1020215943
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1015239002
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1017504697
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1014725152
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1013682110
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1017865293
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1018361249
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1015136549
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1017373297
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1019477368
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1019784679
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1019724260
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1016053693
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1016862976
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1018646513
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1016333756
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1021240719
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1018020878
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1016568024
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1020615699
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1018812345
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1012987657
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1018792903
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1019234371
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1020616464
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1016682822
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1013811378
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1021873628
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1022171113
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1014840682
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1017230067
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1020132840
#Averaging total costs across simulations
TDC_m_alternativeA <- mean(unlist(tot_discounted_costs_m_altA))
TDC_f_alternativeA <- mean(unlist(tot_discounted_costs_f_altA))
#Final result
TDC_alternativeA <- TDC_m_alternativeA + TDC_f_alternativeA
TDC_alternativeA
## [1] 2386420663

The total amount of money that needs to be invested for early detection is:

total_savingsA <- TDC_baseline - TDC_alternativeA
total_savingsA
## [1] -261762070

Consistently with the above discussion, the loss is more moderate than that of alternative scenario A1.

The following is a useful graph to evaluate the trends of P, MPD, APD and D patients over the microsimulation time period:

prepare_plot_data <- function(df_m, scenario) {
  df_m %>%
    as_tibble() %>%
    pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
    count(cycle, state) %>%
    group_by(cycle) %>%
    mutate(percent = n / sum(n)) %>%
    ungroup() %>%
    mutate(scenario = scenario)
}


num_cols_m <- ncol(model_results_m[[50]])
num_cols_m_altA <- ncol(model_results_m_altA[[50]])

colnames(model_results_m[[50]]) <- paste("cycle", 0:(num_cols_m-1), sep = " ")
colnames(model_results_m_altA[[50]]) <- paste("cycle", 0:(num_cols_m_altA-1), sep = " ")


# Baseline
df_m.M <- model_results_m[[50]] %>% prepare_plot_data("Baseline")

# Alternative
df_m.M_altA <- model_results_m_altA[[50]] %>% prepare_plot_data("Alternative")

# Combining
combined_data_mA <- bind_rows(df_m.M, df_m.M_altA)

combined_data1A <- combined_data_mA %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% 
filter(cycle != "cycle 15")

# Plot 
summary_plot_maleA <- ggplot(combined_data1A %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
  geom_line() +
  labs(title = "Comparison of states across cycles and scenarios (Males)",
       x = "Cycle",
       y = "Percentage") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_maleA

The graph for females:

prepare_plot_data <- function(df_m, scenario) {
  df_m %>%
    as_tibble() %>%
    pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
    count(cycle, state) %>%
    group_by(cycle) %>%
    mutate(percent = n / sum(n)) %>%
    ungroup() %>%
    mutate(scenario = scenario)
}


num_cols_f <- ncol(model_results_f[[50]])
num_cols_f_altA <- ncol(model_results_f_altA[[50]])

colnames(model_results_f[[50]]) <- paste("cycle", 0:(num_cols_f-1), sep = " ")
colnames(model_results_f_altA[[50]]) <- paste("cycle", 0:(num_cols_f_altA-1), sep = " ")


# Baseline
df_m.M <- model_results_f[[50]] %>% prepare_plot_data("Baseline")

# Alternative
df_m.M_altA <- model_results_f_altA[[50]] %>% prepare_plot_data("Alternative")

# Combining
combined_data_fA <- bind_rows(df_m.M, df_m.M_altA)

combined_data2A <- combined_data_fA %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% 
filter(cycle != "cycle 15")

# Plot 
summary_plot_femaleA <- ggplot(combined_data2A %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
  geom_line() +
  labs(title = "Comparison of states across cycles and scenarios (Females)",
       x = "Cycle",
       y = "Percentage") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_femaleA

Losses are really prominent from a financial point of view, as indicated by the final result and by the graph comparing costs across scenarios:

However, if the point of view of patients is considered, the previous remarks represent a gain both in terms of life quality and life expectancy.

Let’s evaluate this gain:

process_model_result <- function(model_result) {
  df <- model_result %>% as_tibble()
  cycle_columns <- paste0("cycle ", 0:14)
  map(cycle_columns, ~ df %>% tabyl(!!sym(.x)))
}

# Males
percent_tables_m <- map(model_results_m[1:100], process_model_result)

# Females
percent_tables_f <- map(model_results_f[1:100], process_model_result)

# Aggregate results and compute the averages
aggregate_results <- function(percent_tables) {
  all_states <- c("P", "MPD", "APD", "D")
  cycle_columns <- paste0("cycle ", 0:14)
  
  aggregated <- map(cycle_columns, function(cycle) {
    state_sums <- map_dbl(all_states, function(state) {
      state_n_values <- map_dbl(percent_tables, ~ {
        tabyl_result <- .x[[which(cycle_columns == cycle)]]
        if (state %in% tabyl_result[[1]]) {
          return(tabyl_result$n[tabyl_result[[1]] == state])
        } else {
          return(0)
        }
      })
      mean(state_n_values)
    })
    tibble(state = all_states, mean_n = state_sums)
  })
  
  bind_rows(aggregated, .id = "cycle") %>%
    mutate(cycle = as.numeric(cycle) - 1) 
}

# Aggregate for males
aggregated_m <- aggregate_results(percent_tables_m)

# Aggregate for females
aggregated_f <- aggregate_results(percent_tables_f)

aggregated_m
aggregated_f
#Same approach for the alternative scenario

percent_tables_m_altA <- map(model_results_m_altA[1:100], process_model_result)
percent_tables_f_altA <- map(model_results_f_altA[1:100], process_model_result)

# Aggregate for males
aggregated_m_altA <- aggregate_results(percent_tables_m_altA)

# Aggregate for females
aggregated_f_altA <- aggregate_results(percent_tables_f_altA)

aggregated_m_altA
aggregated_f_altA

With the new tables at hand it is possible to compute the 3 differences that indicate a gain for patients:

library(dplyr)

calculate_differencesA <- function(baseline, alternativeA) {
  baseline %>%
    inner_join(alternativeA, by = c("cycle", "state"), suffix = c("_baseline", "_altA")) %>%
    mutate(
      difference = case_when(
        state == "MPD" ~ mean_n_altA - mean_n_baseline,
        state == "APD" ~ mean_n_baseline - mean_n_altA,
        state == "D" ~ mean_n_baseline - mean_n_altA,
        TRUE ~ NA_real_
      )
    ) %>%
    select(cycle, state, difference) %>%
    filter(!is.na(difference))
}

differences_mA <- calculate_differencesA(aggregated_m, aggregated_m_altA)
differences_fA <- calculate_differencesA(aggregated_f, aggregated_f_altA)

differences_mA
differences_fA

Differences are aggregated with respect to cycles, truncated, since patients have to be counted with integer numbers, and multiplied by 5, since each cycle lasts 5 years.

#Males
summary_mA <- differences_mA %>% 
    group_by(state) %>%
    summarise(
      diff_sum = sum(difference, na.rm = TRUE)
    ) %>%
    mutate(
      diff_sum = floor(diff_sum) * 5
    ) %>%
    select(state, diff_sum)
summary_mA
#Females
summary_fA <- differences_fA %>% 
    group_by(state) %>%
    summarise(
      diff_sum = sum(difference, na.rm = TRUE)
    ) %>%
    mutate(
      diff_sum = floor(diff_sum) * 5
    ) %>%
    select(state, diff_sum)
summary_fA

The previous are the total numbers of years:

The results with respect to the average male or female patient require the previous results to be divided by the total number of male and females patients:

averages_mA <- summary_mA %>%
    mutate(
      diff_sum = (diff_sum)/(n_males)
    ) %>%
    select(state, diff_sum)

averages_fA <- summary_fA %>%
    mutate(
      diff_sum = (diff_sum)/(n_females)
    ) %>%
    select(state, diff_sum)

averages_mA
averages_fA

As expected, the gains in terms of life expectancy are moderate and could be expressed in terms of months. As previously discussed, this gain should not be commented as it represents an artificial gain that does not appear as a hypothesis of alternative scenario A2.

Alternative scenario: B

The alternative scenario B considers a 2-year and a half delay in the onset of APD thanks to AI-based early detection. Physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which consequently reduces the probability of transitioning to the severe stage (P(MPD→APD)).

The increase in P(MPD→MPD) is modelled through the following formula: \[ p^\prime = p^{\frac{60-x}{60}}\]

where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Consequently, the new probability of transitioning to the severe stage is P(MPD→APD) = 1 – p’ – P(MPD→D).

According to the initial hypothesis, x = 12 months and therefore \[ p^\prime=\ p^\frac{60-30}{60}=p^\frac{1}{2} \].

Transition probabilities will be changed accordingly:

library(dplyr)
library(ggplot2)
library(fastmap)
library(purrr)
library(tibble)
library(tidyr)
library(forcats)
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")

generate_transition_matrix_alt_old2 <- function(summary_df, summary_df2, age_classes, gender_name) {
  
  x <- matrix(NA, nrow = 4, ncol = 4)

  x[1, 1] <- 0
  f_prob1 <- f_prob %>% 
    filter(`Age class` == age_class, Gender == gender_name) %>% 
    summarise(f_prob = F) %>% 
    pull(f_prob)
  x[1, 2] <- 1 - f_prob1
  x[1, 3] <- 0
  x[1, 4] <- f_prob1
  
  
   numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  x[2, 1] <- 0
  
  x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD))^(1/2)) 

  x[2, 4] <- numerator_MPD_D / denominator_MPD

  x[2, 2] <- (1 - (numerator_MPD_APD / denominator_MPD - (numerator_MPD_D / denominator_MPD)))^(1/2)

  x[3, 1] <- 0
  x[3, 2] <- 0
  numerator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  
  denominator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
    summarise(n_patients = sum(n_patients)) %>% 
    pull(n_patients)
  
  x[3, 4] <- numerator_APD_D / denominator_APD_D

  x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)

  x[4, 1] <- 0
  x[4, 2] <- 0
  x[4, 3] <- 0
  x[4, 4] <- 1

  return(x)
}

transition_matrices_alt_old2 <- list()

for (gender in genders) {
  for (age_class in age_classes) {
    matrix_name <- paste(gender, age_class, sep = "_")
    transition_matrices_alt_old2[[matrix_name]] <- generate_transition_matrix_alt_old2(summary_df, summary_df2, age_class, gender)
  }
}


transition_matrices_alt_old2
## $`Male_50-54`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9712352 0.00000000 0.02876483
## [2,]    0 0.9707253 0.03222678 0.05000000
## [3,]    0 0.0000000 0.92913386 0.07086614
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_55-59`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9574518 0.00000000 0.04254822
## [2,]    0 0.9928314 0.01031948 0.06938776
## [3,]    0 0.0000000 0.87280702 0.12719298
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Male_60-64`
##      [,1]      [,2]        [,3]       [,4]
## [1,]    0 0.9433756  0.00000000 0.05662437
## [2,]    0 1.0185774 -0.01467027 0.10500000
## [3,]    0 0.0000000  0.81914894 0.18085106
## [4,]    0 0.0000000  0.00000000 1.00000000
## 
## $`Male_65-69`
##      [,1]      [,2]        [,3]       [,4]
## [1,]    0 0.9224868  0.00000000 0.07751319
## [2,]    0 1.0545571 -0.04721619 0.18010076
## [3,]    0 0.0000000  0.69558600 0.30441400
## [4,]    0 0.0000000  0.00000000 1.00000000
## 
## $`Male_70-74`
##      [,1]      [,2]        [,3]      [,4]
## [1,]    0 0.8875735  0.00000000 0.1124265
## [2,]    0 1.0871588 -0.07819291 0.2379693
## [3,]    0 0.0000000  0.57037037 0.4296296
## [4,]    0 0.0000000  0.00000000 1.0000000
## 
## $`Male_75-79`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8201575  0.0000000 0.1798425
## [2,]    0 1.1298357 -0.1162094 0.3262327
## [3,]    0 0.0000000  0.4819977 0.5180023
## [4,]    0 0.0000000  0.0000000 1.0000000
## 
## $`Male_80-84`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.7046099  0.0000000 0.2953901
## [2,]    0 1.1932614 -0.1707038 0.4578714
## [3,]    0 0.0000000  0.3386700 0.6613300
## [4,]    0 0.0000000  0.0000000 1.0000000
## 
## $`Male_85-89`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.5279737  0.0000000 0.4720263
## [2,]    0 1.2670015 -0.2202983 0.6261261
## [3,]    0 0.0000000  0.2570850 0.7429150
## [4,]    0 0.0000000  0.0000000 1.0000000
## 
## $`Male_90-94`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.3260733  0.0000000 0.6739267
## [2,]    0 1.3198820 -0.2387153 0.7531646
## [3,]    0 0.0000000  0.1603053 0.8396947
## [4,]    0 0.0000000  0.0000000 1.0000000
## 
## $`Male_95et+`
##      [,1]     [,2]       [,3]      [,4]
## [1,]    0 0.158585  0.0000000 0.8414150
## [2,]    0 1.359720 -0.2376338 0.8488372
## [3,]    0 0.000000  0.1111111 0.8888889
## [4,]    0 0.000000  0.0000000 1.0000000
## 
## $`Female_50-54`
##      [,1]      [,2]       [,3]       [,4]
## [1,]    0 0.9864538 0.00000000 0.01354618
## [2,]    0 0.9816804 0.01935517 0.02970297
## [3,]    0 0.0000000 0.91935484 0.08064516
## [4,]    0 0.0000000 0.00000000 1.00000000
## 
## $`Female_55-59`
##      [,1]      [,2]         [,3]       [,4]
## [1,]    0 0.9814785  0.000000000 0.01852146
## [2,]    0 1.0057972 -0.004736241 0.05116279
## [3,]    0 0.0000000  0.868852459 0.13114754
## [4,]    0 0.0000000  0.000000000 1.00000000
## 
## $`Female_60-64`
##      [,1]      [,2]        [,3]       [,4]
## [1,]    0 0.9750718 0.000000000 0.02492824
## [2,]    0 0.9943019 0.007222966 0.04829545
## [3,]    0 0.0000000 0.856540084 0.14345992
## [4,]    0 0.0000000 0.000000000 1.00000000
## 
## $`Female_65-69`
##      [,1]      [,2]        [,3]       [,4]
## [1,]    0 0.9644648  0.00000000 0.03553525
## [2,]    0 1.0293222 -0.02647452 0.10743802
## [3,]    0 0.0000000  0.77889447 0.22110553
## [4,]    0 0.0000000  0.00000000 1.00000000
## 
## $`Female_70-74`
##      [,1]      [,2]        [,3]       [,4]
## [1,]    0 0.9455591  0.00000000 0.05444087
## [2,]    0 1.0443199 -0.03928394 0.14899329
## [3,]    0 0.0000000  0.71125265 0.28874735
## [4,]    0 0.0000000  0.00000000 1.00000000
## 
## $`Female_75-79`
##      [,1]      [,2]        [,3]       [,4]
## [1,]    0 0.9040836  0.00000000 0.09591642
## [2,]    0 1.0858177 -0.07802814 0.22950000
## [3,]    0 0.0000000  0.61809816 0.38190184
## [4,]    0 0.0000000  0.00000000 1.00000000
## 
## $`Female_80-84`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.8160931  0.0000000 0.1839069
## [2,]    0 1.1405994 -0.1293833 0.3348106
## [3,]    0 0.0000000  0.4802432 0.5197568
## [4,]    0 0.0000000  0.0000000 1.0000000
## 
## $`Female_85-89`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.6559712  0.0000000 0.3440288
## [2,]    0 1.2108008 -0.1893511 0.4848785
## [3,]    0 0.0000000  0.3756477 0.6243523
## [4,]    0 0.0000000  0.0000000 1.0000000
## 
## $`Female_90-94`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.4385294  0.0000000 0.5614706
## [2,]    0 1.2860712 -0.2380945 0.6597463
## [3,]    0 0.0000000  0.2680412 0.7319588
## [4,]    0 0.0000000  0.0000000 1.0000000
## 
## $`Female_95et+`
##      [,1]      [,2]       [,3]      [,4]
## [1,]    0 0.2311448  0.0000000 0.7688552
## [2,]    0 1.3029974 -0.2429344 0.7032967
## [3,]    0 0.0000000  0.2222222 0.7777778
## [4,]    0 0.0000000  0.0000000 1.0000000
names(transition_matrices_alt_old2) <- NULL  

males_alt_old2 <- transition_matrices_alt_old2[1:10]
females_alt_old2 <- transition_matrices_alt_old2[11:20]

matrices_mf_alt_old2 <- list(males_alt_old2, females_alt_old2)

for (i in 1:length(males_alt_old2)) {
  colnames(males_alt_old2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
  rownames(males_alt_old2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m") 
}
for (i in 1:length(females_alt_old2)) {
  colnames(females_alt_old2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_alt_old2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males_alt_old2)) {
  dimnames(males_alt_old2[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females_alt_old2)) {
  dimnames(females_alt_old2[[i]]) <- list(row_names_f, col_names_f)
}

transition_matrices_mf_alt_old2 <- list(males_alt_old2, females_alt_old2)
transition_matrices_mf_alt_old2
## [[1]]
## [[1]][[1]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9712352 0.00000000 0.02876483
## MPD.m   0 0.9707253 0.03222678 0.05000000
## APD.m   0 0.0000000 0.92913386 0.07086614
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[2]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9574518 0.00000000 0.04254822
## MPD.m   0 0.9928314 0.01031948 0.06938776
## APD.m   0 0.0000000 0.87280702 0.12719298
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[1]][[3]]
##       P.m     MPD.m       APD.m        D.m
## P.m     0 0.9433756  0.00000000 0.05662437
## MPD.m   0 1.0185774 -0.01467027 0.10500000
## APD.m   0 0.0000000  0.81914894 0.18085106
## D.m     0 0.0000000  0.00000000 1.00000000
## 
## [[1]][[4]]
##       P.m     MPD.m       APD.m        D.m
## P.m     0 0.9224868  0.00000000 0.07751319
## MPD.m   0 1.0545571 -0.04721619 0.18010076
## APD.m   0 0.0000000  0.69558600 0.30441400
## D.m     0 0.0000000  0.00000000 1.00000000
## 
## [[1]][[5]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.8875735  0.00000000 0.1124265
## MPD.m   0 1.0871588 -0.07819291 0.2379693
## APD.m   0 0.0000000  0.57037037 0.4296296
## D.m     0 0.0000000  0.00000000 1.0000000
## 
## [[1]][[6]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8201575  0.0000000 0.1798425
## MPD.m   0 1.1298357 -0.1162094 0.3262327
## APD.m   0 0.0000000  0.4819977 0.5180023
## D.m     0 0.0000000  0.0000000 1.0000000
## 
## [[1]][[7]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.7046099  0.0000000 0.2953901
## MPD.m   0 1.1932614 -0.1707038 0.4578714
## APD.m   0 0.0000000  0.3386700 0.6613300
## D.m     0 0.0000000  0.0000000 1.0000000
## 
## [[1]][[8]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.5279737  0.0000000 0.4720263
## MPD.m   0 1.2670015 -0.2202983 0.6261261
## APD.m   0 0.0000000  0.2570850 0.7429150
## D.m     0 0.0000000  0.0000000 1.0000000
## 
## [[1]][[9]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.3260733  0.0000000 0.6739267
## MPD.m   0 1.3198820 -0.2387153 0.7531646
## APD.m   0 0.0000000  0.1603053 0.8396947
## D.m     0 0.0000000  0.0000000 1.0000000
## 
## [[1]][[10]]
##       P.m    MPD.m      APD.m       D.m
## P.m     0 0.158585  0.0000000 0.8414150
## MPD.m   0 1.359720 -0.2376338 0.8488372
## APD.m   0 0.000000  0.1111111 0.8888889
## D.m     0 0.000000  0.0000000 1.0000000
## 
## 
## [[2]]
## [[2]][[1]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9864538 0.00000000 0.01354618
## MPD.f   0 0.9816804 0.01935517 0.02970297
## APD.f   0 0.0000000 0.91935484 0.08064516
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[2]][[2]]
##       P.f     MPD.f        APD.f        D.f
## P.f     0 0.9814785  0.000000000 0.01852146
## MPD.f   0 1.0057972 -0.004736241 0.05116279
## APD.f   0 0.0000000  0.868852459 0.13114754
## D.f     0 0.0000000  0.000000000 1.00000000
## 
## [[2]][[3]]
##       P.f     MPD.f       APD.f        D.f
## P.f     0 0.9750718 0.000000000 0.02492824
## MPD.f   0 0.9943019 0.007222966 0.04829545
## APD.f   0 0.0000000 0.856540084 0.14345992
## D.f     0 0.0000000 0.000000000 1.00000000
## 
## [[2]][[4]]
##       P.f     MPD.f       APD.f        D.f
## P.f     0 0.9644648  0.00000000 0.03553525
## MPD.f   0 1.0293222 -0.02647452 0.10743802
## APD.f   0 0.0000000  0.77889447 0.22110553
## D.f     0 0.0000000  0.00000000 1.00000000
## 
## [[2]][[5]]
##       P.f     MPD.f       APD.f        D.f
## P.f     0 0.9455591  0.00000000 0.05444087
## MPD.f   0 1.0443199 -0.03928394 0.14899329
## APD.f   0 0.0000000  0.71125265 0.28874735
## D.f     0 0.0000000  0.00000000 1.00000000
## 
## [[2]][[6]]
##       P.f     MPD.f       APD.f        D.f
## P.f     0 0.9040836  0.00000000 0.09591642
## MPD.f   0 1.0858177 -0.07802814 0.22950000
## APD.f   0 0.0000000  0.61809816 0.38190184
## D.f     0 0.0000000  0.00000000 1.00000000
## 
## [[2]][[7]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.8160931  0.0000000 0.1839069
## MPD.f   0 1.1405994 -0.1293833 0.3348106
## APD.f   0 0.0000000  0.4802432 0.5197568
## D.f     0 0.0000000  0.0000000 1.0000000
## 
## [[2]][[8]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.6559712  0.0000000 0.3440288
## MPD.f   0 1.2108008 -0.1893511 0.4848785
## APD.f   0 0.0000000  0.3756477 0.6243523
## D.f     0 0.0000000  0.0000000 1.0000000
## 
## [[2]][[9]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.4385294  0.0000000 0.5614706
## MPD.f   0 1.2860712 -0.2380945 0.6597463
## APD.f   0 0.0000000  0.2680412 0.7319588
## D.f     0 0.0000000  0.0000000 1.0000000
## 
## [[2]][[10]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.2311448  0.0000000 0.7688552
## MPD.f   0 1.3029974 -0.2429344 0.7032967
## APD.f   0 0.0000000  0.2222222 0.7777778
## D.f     0 0.0000000  0.0000000 1.0000000
transition_matrices_m_alt_old2 <- transition_matrices_mf_alt_old2[[1]]
transition_matrices_f_alt_old2 <- transition_matrices_mf_alt_old2[[2]]

extract_rows_as_named_list <- function(matrix) {
  list(
    P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
    MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
    APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
    D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
  )
}

transition_prob_m_alt_old2 <- lapply(transition_matrices_m_alt_old2, extract_rows_as_named_list)

transition_prob_f_alt_old2 <- lapply(transition_matrices_f_alt_old2, extract_rows_as_named_list)

print("Transition Probabilities for Males:")
## [1] "Transition Probabilities for Males:"
print(transition_prob_m_alt_old2)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.97072534 0.03222678 0.05000000 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.99283145 0.01031948 0.06938776 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##           P         MPD         APD           D 
##  0.00000000  1.01857744 -0.01467027  0.10500000 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##           P         MPD         APD           D 
##  0.00000000  1.05455710 -0.04721619  0.18010076 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##           P         MPD         APD           D 
##  0.00000000  1.08715883 -0.07819291  0.23796933 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.1298357 -0.1162094  0.3262327 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.1932614 -0.1707038  0.4578714 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.2670015 -0.2202983  0.6261261 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.3198820 -0.2387153  0.7531646 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##        P      MPD      APD        D 
## 0.000000 0.158585 0.000000 0.841415 
## 
## [[10]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.3597195 -0.2376338  0.8488372 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Transition Probabilities for Females:")
## [1] "Transition Probabilities for Females:"
print(transition_prob_f_alt_old2)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.98168038 0.01935517 0.02970297 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##            P          MPD          APD            D 
##  0.000000000  1.005797150 -0.004736241  0.051162791 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.994301948 0.007222966 0.048295455 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##           P         MPD         APD           D 
##  0.00000000  1.02932217 -0.02647452  0.10743802 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##           P         MPD         APD           D 
##  0.00000000  1.04431989 -0.03928394  0.14899329 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
##  0.00000000  1.08581766 -0.07802814  0.22950000 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.1405994 -0.1293833  0.3348106 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.2108008 -0.1893511  0.4848785 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.2860712 -0.2380945  0.6597463 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2311448 0.0000000 0.7688552 
## 
## [[10]]$MPD
##          P        MPD        APD          D 
##  0.0000000  1.3029974 -0.2429344  0.7032967 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1

The graph showcasing probabilities of death with respect to severity:

severity_labels <- c("Prodromal", "Mild", "Advanced")

# Extracting probabilities of death from matrices
extract_probabilities <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[1],
      probability_of_death = matrix[1, 4]
    ))
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_death = matrix[2, 4]
    ))
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[3],
      probability_of_death = matrix[3, 4]
    ))
  }
  
  return(data)
}

# Extracting data for males/females
males_data_alt_old2 <- extract_probabilities(males_alt_old2, age_classes, "Male")
females_data_alt_old2 <- extract_probabilities(females_alt_old2, age_classes, "Female")

final_data_alt_old2 <- rbind(males_data_alt_old2, females_data_alt_old2)

# Let's apply the adjustment
final_data_alt1_old2 <- final_data_alt_old2 %>%
  group_by(gender) %>% 
  mutate(probability_of_death = ifelse(
    age_class == "95et+" & severity == "Prodromal",
    probability_of_death[age_class == "95et+" & severity == "Mild"] -
      (probability_of_death[age_class == "90-94" & severity == "Mild"] -
       probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
    probability_of_death
  ))

graph_prob_mf_alt2 <- ggplot(final_data_alt1_old2, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
  theme_minimal() +
  labs(title = "Probability of death with respect to severity, alternative scenario",
       x = "Age class",
       y = "Probability",
       color = "Severity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf_alt2

Considering the alternative scenario B, the proposed assumption is to consider a 2-year and a half delay in the onset of APD thanks to AI-based early detection. The manipulation of the prodromal period should not be considered as this stage cannot be precisely detected by definition, as well as the rigor of the criteria used to distinguish between MPD and APD should not be varied as such variation is already used to tackle the issue related to the unclear definition of APD. The new approach suggests that physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which proportionally reduces the probability of transitioning to the severe stage (P(MPD→APD)) and the probability of dying (P(MPD→D)). The increase in P(MPD→MPD) is modeled through the following formula:

\[ p^\prime=\ p^\frac{60-x}{60} \]

where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Accordingly, the positive gain in P(MPD→APD) is defined as:

\[ \mathrm{\Delta}\ =\ p^\prime\ -\ p \] This gain is counterbalanced by a proportional redistribution of its negative value, - delta, among the other two transition probabilities having MPD as the initial state, namely P(MPD→APD) and P(MPD→D). For this purpose, the negative gain is decomposed into:

\[ -\ \mathrm{\Delta}\ =\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD})\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]

The two components are proportional to the initial probabilities computed in the baseline scenario:

\[ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{APD})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \] \[ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{D})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \]

Consequently, the new probabilities for the alternative scenario are defined as:

\[ p'(\mathrm{MPD} \rightarrow \mathrm{APD}) = p(\mathrm{MPD} \rightarrow \mathrm{APD}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) \]

\[ p'(\mathrm{MPD} \rightarrow \mathrm{D}) = p(\mathrm{MPD} \rightarrow \mathrm{D}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]

In this way, the sum to 1 for the second row of the transition matrices is ensured in the alternative scenario B.

# Adjust probability_of_death for 95+ patients
final_data1_alt2 <- final_data_alt_old2 %>%
  group_by(gender) %>%
  mutate(probability_of_death = ifelse(
    age_class == "95et+" & severity == "Prodromal",
    probability_of_death[age_class == "95et+" & severity == "Mild"] -
      (probability_of_death[age_class == "90-94" & severity == "Mild"] -
       probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
    probability_of_death
  ))

age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")

# Update f_prob1 with correct probabilities
f_prob12 <- f_prob %>%
  mutate(
    F = case_when(
      `Age class` == "95et+" & Gender == "Male" ~ final_data1_alt2 %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      `Age class` == "95et+" & Gender == "Female" ~ final_data1_alt2 %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
      TRUE ~ F
    )
  )

# Function to generate transition matrix
generate_transition_matrix_altB <- function(summary_df, summary_df2, final_data1_alt, age_class, gender_name) {
  x <- matrix(NA, nrow = 4, ncol = 4)
  x[1, 1] <- 0

  f_prob1B <- f_prob12 %>%
    filter(`Age class` == age_class & Gender == gender_name) %>%
    pull(F)
   
  x[1, 2] <- 1 - f_prob1B
  x[1, 3] <- 0
  x[1, 4] <- f_prob1B

  x[2, 1] <- 0

  numerator_MPD_APD <- summary_df1 %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  numerator_MPD_D <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  denominator_MPD <- summary_df %>%
    filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)
  
     if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
    x[2, 4] <- numerator_MPD_D / denominator_MPD
  } else {
    x[2, 4] <- NA
  }

  if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
    x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((numerator_MPD_MPD / denominator_MPD)^(1/2))
  } else {
    x[2, 3] <- NA
  }

  x[2, 2] <- ifelse(length(numerator_MPD_MPD) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0, 
                    (numerator_MPD_MPD / denominator_MPD)^(1/2), NA)

  x[3, 1] <- 0
  x[3, 2] <- 0
  numerator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)
  
  denominator_APD_D <- summary_df2 %>%
    filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
    summarise(n_patients = sum(n_patients)) %>%
    pull(n_patients)

  if (length(numerator_APD_D) > 0 && length(denominator_APD_D) > 0 && denominator_APD_D != 0) {
    x[3, 4] <- numerator_APD_D / denominator_APD_D
    x[3, 3] <- 1 - x[3, 4]
  } else {
    x[3, 4] <- NA
    x[3, 3] <- NA
  }

  x[4, 1] <- 0
  x[4, 2] <- 0
  x[4, 3] <- 0
  x[4, 4] <- 1

  return(x)
}

transition_matrices_alt1B <- list()

for (gender in genders) {
  for (age_class in age_classes) {
    matrix_name <- paste(gender, age_class, sep = "_")
    transition_matrices_alt1B[[matrix_name]] <- generate_transition_matrix_altB(summary_df, summary_df2, final_data1_alt, age_class, gender)
  }
}

names(transition_matrices_alt1B) <- NULL  

males_alt1B <- transition_matrices_alt1B[1:10]
females_alt1B <- transition_matrices_alt1B[11:20]

matrices_mf_alt1B <- list(males_alt1B, females_alt1B)

for (i in 1:length(males_alt1B)) {
  colnames(males_alt1B[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  rownames(males_alt1B[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt1B)) {
  colnames(females_alt1B[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_alt1B[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}

transition_matrices_m_alt1B <- matrices_mf_alt1B[[1]]
transition_matrices_f_alt1B <- matrices_mf_alt1B[[2]]

extract_rows_as_named_list <- function(matrix) {
  list(
    P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
    MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
    APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
    D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
  )
}

transition_prob_m_alt1B <- lapply(transition_matrices_m_alt1B, extract_rows_as_named_list)
transition_prob_f_alt1B <- lapply(transition_matrices_f_alt1B, extract_rows_as_named_list)

print("Transition Probabilities for Males:")
## [1] "Transition Probabilities for Males:"
print(transition_prob_m_alt1B)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91777322 0.03222678 0.05000000 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92029277 0.01031948 0.06938776 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.90967027 -0.01467027  0.10500000 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.86711543 -0.04721619  0.18010076 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.84022359 -0.07819291  0.23796933 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.7899767 -0.1162094  0.3262327 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.7128324 -0.1707038  0.4578714 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.5941721 -0.2202983  0.6261261 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.4855507 -0.2387153  0.7531646 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2304007 0.0000000 0.7695993 
## 
## [[10]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.3887966 -0.2376338  0.8488372 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Transition Probabilities for Females:")
## [1] "Transition Probabilities for Females:"
print(transition_prob_f_alt1B)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.95094186 0.01935517 0.02970297 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##            P          MPD          APD            D 
##  0.000000000  0.953573451 -0.004736241  0.051162791 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.944481580 0.007222966 0.048295455 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.91903651 -0.02647452  0.10743802 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.89029065 -0.03928394  0.14899329 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##           P         MPD         APD           D 
##  0.00000000  0.84852814 -0.07802814  0.22950000 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.7945726 -0.1293833  0.3348106 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.7044726 -0.1893511  0.4848785 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.5783483 -0.2380945  0.6597463 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3949789 0.0000000 0.6050211 
## 
## [[10]]$MPD
##          P        MPD        APD          D 
##  0.0000000  0.5396376 -0.2429344  0.7032967 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
# Function to calculate delta
calculate_delta <- function(baseline, alt) {
  delta <- alt - baseline
  return(delta)
}

# Function to update transition probabilities based on delta distribution
update_transition_probabilitiesB <- function(transition_prob_m, transition_prob_f, transition_prob_m_alt1B, transition_prob_f_alt1B) {
  for (i in 1:length(transition_prob_m)) {
    # Extract baseline and alternative matrices
    baseline_matrix_m <- transition_prob_m[[i]]$MPD
    alt_matrix_m <- transition_prob_m_alt1B[[i]]$MPD
    baseline_matrix_f <- transition_prob_f[[i]]$MPD
    alt_matrix_f <- transition_prob_f_alt1B[[i]]$MPD
    
    # Baseline and alternative [2,2] elements
    baseline_m_MPD <- baseline_matrix_m["MPD"]
    alt_m_MPD <- alt_matrix_m["MPD"]
    
    baseline_f_MPD <- baseline_matrix_f["MPD"]
    alt_f_MPD <- alt_matrix_f["MPD"]
    
    # Calculate deltas
    delta_m <- calculate_delta(baseline_m_MPD, alt_m_MPD)
    delta_f <- calculate_delta(baseline_f_MPD, alt_f_MPD)
    
    # Calculate baseline probabilities
    p_m_APD <- baseline_matrix_m["APD"]
    p_m_D <- baseline_matrix_m["D"]
    p_f_APD <- baseline_matrix_f["APD"]
    p_f_D <- baseline_matrix_f["D"]
    
    # Calculate delta distribution for males
    sum_m_APD_D <- p_m_APD + p_m_D
    delta_m_APD <- (p_m_APD / sum_m_APD_D) * delta_m
    delta_m_D <- (p_m_D / sum_m_APD_D) * delta_m
    
    # Calculate delta distribution for females
    sum_f_APD_D <- p_f_APD + p_f_D
    delta_f_APD <- (p_f_APD / sum_f_APD_D) * delta_f
    delta_f_D <- (p_f_D / sum_f_APD_D) * delta_f
    
    # Update alternative transition probabilities for males
    transition_prob_m_alt1B[[i]]$MPD["APD"] <- baseline_matrix_m["APD"] - delta_m_APD
    transition_prob_m_alt1B[[i]]$MPD["D"] <- baseline_matrix_m["D"] - delta_m_D
    
    # Update alternative transition probabilities for females
    transition_prob_f_alt1B[[i]]$MPD["APD"] <- baseline_matrix_f["APD"] - delta_f_APD
    transition_prob_f_alt1B[[i]]$MPD["D"] <- baseline_matrix_f["D"] - delta_f_D
  }
  return(list(transition_prob_m_alt1B, transition_prob_f_alt1B))
}

# Call the function to update transition probabilities
updated_transition_probsB <- update_transition_probabilitiesB(transition_prob_m, transition_prob_f, transition_prob_m_alt1B, transition_prob_f_alt1B)
transition_prob_m_altB <- updated_transition_probsB[[1]]
transition_prob_f_altB <- updated_transition_probsB[[2]]

print("Updated Transition Probabilities for Males (Alternative Scenario):")
## [1] "Updated Transition Probabilities for Males (Alternative Scenario):"
print(transition_prob_m_altB)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97123517 0.00000000 0.02876483 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91777322 0.05615487 0.02607190 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.92913386 0.07086614 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.95745178 0.00000000 0.04254822 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.92029277 0.04357329 0.03613395 
## 
## [[2]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.872807 0.127193 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94337563 0.00000000 0.05662437 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.90967027 0.03534642 0.05498331 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8191489 0.1808511 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.92248681 0.00000000 0.07751319 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.86711543 0.03642521 0.09645936 
## 
## [[4]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.695586 0.304414 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8875735 0.0000000 0.1124265 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.84022359 0.03046097 0.12931544 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.5703704 0.4296296 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8201575 0.0000000 0.1798425 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.78997666 0.02776804 0.18225531 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4819977 0.5180023 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.7046099 0.0000000 0.2953901 
## 
## [[7]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.7128324 0.0198493 0.2673183 
## 
## [[7]]$APD
##       P     MPD     APD       D 
## 0.00000 0.00000 0.33867 0.66133 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.5279737 0.0000000 0.4720263 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.59417215 0.01306843 0.39275942 
## 
## [[8]]$APD
##        P      MPD      APD        D 
## 0.000000 0.000000 0.257085 0.742915 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3260733 0.0000000 0.6739267 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.485550712 0.007455787 0.506993501 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1603053 0.8396947 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.2304007 0.0000000 0.7695993 
## 
## [[10]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.3887966 0.0000000 0.6112034 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.1111111 0.8888889 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
print("Updated Transition Probabilities for Females (Alternative Scenario):")
## [1] "Updated Transition Probabilities for Females (Alternative Scenario):"
print(transition_prob_f_altB)
## [[1]]
## [[1]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98645382 0.00000000 0.01354618 
## 
## [[1]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.95094186 0.03383320 0.01522494 
## 
## [[1]]$APD
##          P        MPD        APD          D 
## 0.00000000 0.00000000 0.91935484 0.08064516 
## 
## [[1]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[2]]
## [[2]]$P
##          P        MPD        APD          D 
## 0.00000000 0.98147854 0.00000000 0.01852146 
## 
## [[2]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.95357345 0.02023721 0.02618934 
## 
## [[2]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8688525 0.1311475 
## 
## [[2]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[3]]
## [[3]]$P
##          P        MPD        APD          D 
## 0.00000000 0.97507176 0.00000000 0.02492824 
## 
## [[3]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.94448158 0.03068123 0.02483719 
## 
## [[3]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.8565401 0.1434599 
## 
## [[3]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[4]]
## [[4]]$P
##          P        MPD        APD          D 
## 0.00000000 0.96446475 0.00000000 0.03553525 
## 
## [[4]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.91903651 0.02497810 0.05598539 
## 
## [[4]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7788945 0.2211055 
## 
## [[4]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[5]]
## [[5]]$P
##          P        MPD        APD          D 
## 0.00000000 0.94555913 0.00000000 0.05444087 
## 
## [[5]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.89029065 0.03088904 0.07882031 
## 
## [[5]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.7112527 0.2887473 
## 
## [[5]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[6]]
## [[6]]$P
##          P        MPD        APD          D 
## 0.00000000 0.90408358 0.00000000 0.09591642 
## 
## [[6]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.84852814 0.02731903 0.12415283 
## 
## [[6]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.6180982 0.3819018 
## 
## [[6]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[7]]
## [[7]]$P
##         P       MPD       APD         D 
## 0.0000000 0.8160931 0.0000000 0.1839069 
## 
## [[7]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.7945726 0.0188589 0.1865684 
## 
## [[7]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.4802432 0.5197568 
## 
## [[7]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[8]]
## [[8]]$P
##         P       MPD       APD         D 
## 0.0000000 0.6559712 0.0000000 0.3440288 
## 
## [[8]]$MPD
##          P        MPD        APD          D 
## 0.00000000 0.70447257 0.01105319 0.28447424 
## 
## [[8]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.3756477 0.6243523 
## 
## [[8]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[9]]
## [[9]]$P
##         P       MPD       APD         D 
## 0.0000000 0.4385294 0.0000000 0.5614706 
## 
## [[9]]$MPD
##           P         MPD         APD           D 
## 0.000000000 0.578348283 0.003653828 0.417997890 
## 
## [[9]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2680412 0.7319588 
## 
## [[9]]$D
##   P MPD APD   D 
##   0   0   0   1 
## 
## 
## [[10]]
## [[10]]$P
##         P       MPD       APD         D 
## 0.0000000 0.3949789 0.0000000 0.6050211 
## 
## [[10]]$MPD
##         P       MPD       APD         D 
## 0.0000000 0.5396376 0.0035687 0.4567937 
## 
## [[10]]$APD
##         P       MPD       APD         D 
## 0.0000000 0.0000000 0.2222222 0.7777778 
## 
## [[10]]$D
##   P MPD APD   D 
##   0   0   0   1
males_altB <- lapply(transition_prob_m_altB, function(prob) {
  matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
females_altB <- lapply(transition_prob_f_altB, function(prob) {
  matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})

for (i in 1:length(males_altB)) {
  colnames(males_altB[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
  rownames(males_altB[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_altB)) {
  colnames(females_altB[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
  rownames(females_altB[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}

print("Updated Transition Matrices for Males (Alternative Scenario):")
## [1] "Updated Transition Matrices for Males (Alternative Scenario):"
print(males_altB)
## [[1]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9712352 0.00000000 0.02876483
## MPD.m   0 0.9177732 0.05615487 0.02607190
## APD.m   0 0.0000000 0.92913386 0.07086614
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[2]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9574518 0.00000000 0.04254822
## MPD.m   0 0.9202928 0.04357329 0.03613395
## APD.m   0 0.0000000 0.87280702 0.12719298
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[3]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9433756 0.00000000 0.05662437
## MPD.m   0 0.9096703 0.03534642 0.05498331
## APD.m   0 0.0000000 0.81914894 0.18085106
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[4]]
##       P.m     MPD.m      APD.m        D.m
## P.m     0 0.9224868 0.00000000 0.07751319
## MPD.m   0 0.8671154 0.03642521 0.09645936
## APD.m   0 0.0000000 0.69558600 0.30441400
## D.m     0 0.0000000 0.00000000 1.00000000
## 
## [[5]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8875735 0.00000000 0.1124265
## MPD.m   0 0.8402236 0.03046097 0.1293154
## APD.m   0 0.0000000 0.57037037 0.4296296
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[6]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.8201575 0.00000000 0.1798425
## MPD.m   0 0.7899767 0.02776804 0.1822553
## APD.m   0 0.0000000 0.48199768 0.5180023
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[7]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.7046099 0.0000000 0.2953901
## MPD.m   0 0.7128324 0.0198493 0.2673183
## APD.m   0 0.0000000 0.3386700 0.6613300
## D.m     0 0.0000000 0.0000000 1.0000000
## 
## [[8]]
##       P.m     MPD.m      APD.m       D.m
## P.m     0 0.5279737 0.00000000 0.4720263
## MPD.m   0 0.5941721 0.01306843 0.3927594
## APD.m   0 0.0000000 0.25708502 0.7429150
## D.m     0 0.0000000 0.00000000 1.0000000
## 
## [[9]]
##       P.m     MPD.m       APD.m       D.m
## P.m     0 0.3260733 0.000000000 0.6739267
## MPD.m   0 0.4855507 0.007455787 0.5069935
## APD.m   0 0.0000000 0.160305344 0.8396947
## D.m     0 0.0000000 0.000000000 1.0000000
## 
## [[10]]
##       P.m     MPD.m     APD.m       D.m
## P.m     0 0.2304007 0.0000000 0.7695993
## MPD.m   0 0.3887966 0.0000000 0.6112034
## APD.m   0 0.0000000 0.1111111 0.8888889
## D.m     0 0.0000000 0.0000000 1.0000000
print("Updated Transition Matrices for Females (Alternative Scenario):")
## [1] "Updated Transition Matrices for Females (Alternative Scenario):"
print(females_altB)
## [[1]]
##       P.f     MPD.f     APD.f        D.f
## P.f     0 0.9864538 0.0000000 0.01354618
## MPD.f   0 0.9509419 0.0338332 0.01522494
## APD.f   0 0.0000000 0.9193548 0.08064516
## D.f     0 0.0000000 0.0000000 1.00000000
## 
## [[2]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9814785 0.00000000 0.01852146
## MPD.f   0 0.9535735 0.02023721 0.02618934
## APD.f   0 0.0000000 0.86885246 0.13114754
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[3]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9750718 0.00000000 0.02492824
## MPD.f   0 0.9444816 0.03068123 0.02483719
## APD.f   0 0.0000000 0.85654008 0.14345992
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[4]]
##       P.f     MPD.f     APD.f        D.f
## P.f     0 0.9644648 0.0000000 0.03553525
## MPD.f   0 0.9190365 0.0249781 0.05598539
## APD.f   0 0.0000000 0.7788945 0.22110553
## D.f     0 0.0000000 0.0000000 1.00000000
## 
## [[5]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9455591 0.00000000 0.05444087
## MPD.f   0 0.8902907 0.03088904 0.07882031
## APD.f   0 0.0000000 0.71125265 0.28874735
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[6]]
##       P.f     MPD.f      APD.f        D.f
## P.f     0 0.9040836 0.00000000 0.09591642
## MPD.f   0 0.8485281 0.02731903 0.12415283
## APD.f   0 0.0000000 0.61809816 0.38190184
## D.f     0 0.0000000 0.00000000 1.00000000
## 
## [[7]]
##       P.f     MPD.f     APD.f       D.f
## P.f     0 0.8160931 0.0000000 0.1839069
## MPD.f   0 0.7945726 0.0188589 0.1865684
## APD.f   0 0.0000000 0.4802432 0.5197568
## D.f     0 0.0000000 0.0000000 1.0000000
## 
## [[8]]
##       P.f     MPD.f      APD.f       D.f
## P.f     0 0.6559712 0.00000000 0.3440288
## MPD.f   0 0.7044726 0.01105319 0.2844742
## APD.f   0 0.0000000 0.37564767 0.6243523
## D.f     0 0.0000000 0.00000000 1.0000000
## 
## [[9]]
##       P.f     MPD.f       APD.f       D.f
## P.f     0 0.4385294 0.000000000 0.5614706
## MPD.f   0 0.5783483 0.003653828 0.4179979
## APD.f   0 0.0000000 0.268041237 0.7319588
## D.f     0 0.0000000 0.000000000 1.0000000
## 
## [[10]]
##       P.f     MPD.f     APD.f       D.f
## P.f     0 0.3949789 0.0000000 0.6050211
## MPD.f   0 0.5396376 0.0035687 0.4567937
## APD.f   0 0.0000000 0.2222222 0.7777778
## D.f     0 0.0000000 0.0000000 1.0000000

The graph showcasing probabilities of remaining MPD:

extract_probabilities2_alt <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2]
    ))
    
  }
  
  return(data)
}

males_data_rem_altB <- extract_probabilities2_alt(males_altB, age_classes, "Male")
females_data_rem_altB <- extract_probabilities2_alt(females_altB, age_classes, "Female")

final_data_rem_altB <- rbind(males_data_rem_altB, females_data_rem_altB)

graph_prob_mf_rem_altB <- ggplot(final_data_rem_altB, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD with respect to gender and age classes, alternative scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_altB

The graph showcasing probabilities of transitioning from MPD to APD is:

extract_probabilities1 <- function(matrices, age_classes, genders) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3]
    ))
    
  }
  
  return(data)
}

males_data_tra_altB <- extract_probabilities1(males_altB, age_classes, "Male")
females_data_tra_altB <- extract_probabilities1(females_altB, age_classes, "Female")

final_data_tra_altB <- rbind(males_data_tra_altB, females_data_tra_altB)

graph_prob_mf_tra_altB <- ggplot(final_data_tra_altB, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, alternative scenario",
       x = "Age class",
       y = "Probability") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_altB

Comparison across scenarios (probability of remaining MPD):

extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_remainingMPD = matrix[2, 2],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_rem_comb <- extract_probabilities_comb1(males, age_classes, "Male", "Baseline")
females_data_rem_comb <- extract_probabilities_comb1(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_rem_alt_combB <- extract_probabilities_comb1(males_altB, age_classes, "Male", "Alternative B")
females_data_rem_alt_combB <- extract_probabilities_comb1(females_altB, age_classes, "Female", "Alternative B")

# Combine all data
final_data_rem_combB <- rbind(males_data_rem_comb, females_data_rem_comb, males_data_rem_alt_combB, females_data_rem_alt_combB)

# Create the combined graph
graph_prob_mf_rem_combinedB <- ggplot(final_data_rem_combB, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of remaining MPD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_rem_combinedB

Comparison across scenarios (probability of transitioning from MPD to APD):

extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_transitioning = matrix[2, 3],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_tra_comb <- extract_probabilities_comb2(males, age_classes, "Male", "Baseline")
females_data_tra_comb <- extract_probabilities_comb2(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_tra_alt_combB <- extract_probabilities_comb2(males_altB, age_classes, "Male", "Alternative B")
females_data_tra_alt_combB <- extract_probabilities_comb2(females_altB, age_classes, "Female", "Alternative B")

# Combine all data
final_data_tra_combB <- rbind(males_data_tra_comb, females_data_tra_comb, males_data_tra_alt_combB, females_data_tra_alt_combB)

# Create the combined graph
graph_prob_mf_tra_combinedB <- ggplot(final_data_tra_combB, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of transitioning from MPD to APD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_tra_combinedB

Comparison across scenarios (probability of dying when MPD):

extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
  data <- data.frame()
  
  for (i in 1:length(matrices)) {
    age_class <- age_classes[i]
    matrix <- matrices[[i]]
    
    data <- rbind(data, data.frame(
      age_class = age_class,
      gender = genders,
      severity = severity_labels[2],
      probability_of_dyingMPD = matrix[2, 4],
      scenario = scenario
    ))
    
  }
  
  return(data)
}

# Extract data for baseline scenario
males_data_die_comb <- extract_probabilities_comb3(males, age_classes, "Male", "Baseline")
females_data_die_comb <- extract_probabilities_comb3(females, age_classes, "Female", "Baseline")

# Extract data for alternative scenario
males_data_die_alt_combB <- extract_probabilities_comb3(males_altB, age_classes, "Male", "Alternative B")
females_data_die_alt_combB <- extract_probabilities_comb3(females_altB, age_classes, "Female", "Alternative B")

# Combine all data
final_data_die_combB <- rbind(males_data_die_comb, females_data_die_comb, males_data_die_alt_combB, females_data_die_alt_combB)

# Create the combined graph
graph_prob_mf_die_combinedB <- ggplot(final_data_die_combB, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ gender) +
  theme_minimal() +
  labs(title = "Probability of dying when MPD: comparison across scenarios",
       x = "Age class",
       y = "Probability",
       colour = "Scenario") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))

graph_prob_mf_die_combinedB

The new version of the microsimulation model is to be initialized:

n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 period

Costs in alternative scenarios are slightly different from those of the baseline scenario due to anticipation in the detection of the disease. In particular, the 1-year gain in delaying the onset of PD is associated with an early detection of 2 years (note2: why?), resulting in an early treatment of prodromal patients. All patients begin the model as prodromal in “cycle 0”, after which they either transition to MPD or pass away in “cycle 1” and this means that these patients are treated 2 years in advance before the beginning of “cycle 1”. Accordingly, the additional medical expense is equal to the 2 fifths of “c”, which is the average extra cost of a MPD patient during the 5-year cycle of the model.

#Males
transition_costs_m_alt <- list()
for (cycle in 1:10) {
  c.P.m <- costs_model_males[[cycle, "cp"]] + ((2/5)*costs_model_males[[cycle, "c"]])
  c.MPD.m <- costs_model_males[[cycle, "c"]]
  c.APD.m <- costs_model_males[[cycle, "C"]]
  c.D.m <- costs_model_males[[cycle, "D"]]
  transition_costs_m_alt[[cycle]] <- list(
    "P" = c(c.P.m),
    "MPD" = c(c.MPD.m),
    "APD" = c(c.APD.m),
    "D" = c(c.D.m)
  )
  
}

#Costs are repeated for 95+
last_transition_m_alt <- transition_costs_m_alt[[10]]
for (i in 11:n.t) {
  transition_costs_m_alt[[i]] <- last_transition_m_alt
}
print(transition_costs_m_alt)
## [[1]]
## [[1]]$P
## [1] 28260.64
## 
## [[1]]$MPD
## [1] 30039.15
## 
## [[1]]$APD
## [1] 82777.9
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 27026.7
## 
## [[2]]$MPD
## [1] 18805.09
## 
## [[2]]$APD
## [1] 52417.23
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 24032.15
## 
## [[3]]$MPD
## [1] 14841.59
## 
## [[3]]$APD
## [1] 54636.55
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 27575
## 
## [[4]]$MPD
## [1] 18675.96
## 
## [[4]]$APD
## [1] 46795.03
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 31487.79
## 
## [[5]]$MPD
## [1] 18764.37
## 
## [[5]]$APD
## [1] 45958.37
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 34797.93
## 
## [[6]]$MPD
## [1] 17788
## 
## [[6]]$APD
## [1] 36210.67
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 37455.06
## 
## [[7]]$MPD
## [1] 15104.06
## 
## [[7]]$APD
## [1] 33332.77
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 37602.5
## 
## [[8]]$MPD
## [1] 9020.232
## 
## [[8]]$APD
## [1] 23602.49
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 36466.5
## 
## [[9]]$MPD
## [1] 5341.272
## 
## [[9]]$APD
## [1] 19485.06
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 33886.03
## 
## [[10]]$MPD
## [1] 6355.477
## 
## [[10]]$APD
## [1] 0
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 33886.03
## 
## [[11]]$MPD
## [1] 6355.477
## 
## [[11]]$APD
## [1] 0
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 33886.03
## 
## [[12]]$MPD
## [1] 6355.477
## 
## [[12]]$APD
## [1] 0
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 33886.03
## 
## [[13]]$MPD
## [1] 6355.477
## 
## [[13]]$APD
## [1] 0
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 33886.03
## 
## [[14]]$MPD
## [1] 6355.477
## 
## [[14]]$APD
## [1] 0
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 33886.03
## 
## [[15]]$MPD
## [1] 6355.477
## 
## [[15]]$APD
## [1] 0
## 
## [[15]]$D
## [1] 0
#Females
transition_costs_f_alt <- list()
for (cycle in 1:10) {
  c.P.f <- costs_model_females[[cycle, "cp"]] + ((2/5)*costs_model_females[[cycle, "c"]])
  c.MPD.f <- costs_model_females[[cycle, "c"]]
  c.APD.f <- costs_model_females[[cycle, "C"]]
  c.D.f <- costs_model_females[[cycle, "D"]]
  transition_costs_f_alt[[cycle]] <- list(
    "P" = c(c.P.f),
    "MPD" = c(c.MPD.f),
    "APD" = c(c.APD.f),
    "D" = c(c.D.f)
  )
  
}

#Costs are repeated for 95+
last_transition_f_alt <- transition_costs_f_alt[[10]]
for (i in 11:n.t) {
  transition_costs_f_alt[[i]] <- last_transition_f_alt
}

print(transition_costs_f_alt)
## [[1]]
## [[1]]$P
## [1] 25124.56
## 
## [[1]]$MPD
## [1] 24292.53
## 
## [[1]]$APD
## [1] 55993.02
## 
## [[1]]$D
## [1] 0
## 
## 
## [[2]]
## [[2]]$P
## [1] 26874.58
## 
## [[2]]$MPD
## [1] 24368.35
## 
## [[2]]$APD
## [1] 66431.63
## 
## [[2]]$D
## [1] 0
## 
## 
## [[3]]
## [[3]]$P
## [1] 21895.67
## 
## [[3]]$MPD
## [1] 16594.83
## 
## [[3]]$APD
## [1] 64962.58
## 
## [[3]]$D
## [1] 0
## 
## 
## [[4]]
## [[4]]$P
## [1] 22633.31
## 
## [[4]]$MPD
## [1] 15286.68
## 
## [[4]]$APD
## [1] 50340.51
## 
## [[4]]$D
## [1] 0
## 
## 
## [[5]]
## [[5]]$P
## [1] 28864.52
## 
## [[5]]$MPD
## [1] 21780.85
## 
## [[5]]$APD
## [1] 34621.54
## 
## [[5]]$D
## [1] 0
## 
## 
## [[6]]
## [[6]]$P
## [1] 31653.34
## 
## [[6]]$MPD
## [1] 18533.03
## 
## [[6]]$APD
## [1] 41807.45
## 
## [[6]]$D
## [1] 0
## 
## 
## [[7]]
## [[7]]$P
## [1] 36832.21
## 
## [[7]]$MPD
## [1] 19459.15
## 
## [[7]]$APD
## [1] 42848.83
## 
## [[7]]$D
## [1] 0
## 
## 
## [[8]]
## [[8]]$P
## [1] 38166.8
## 
## [[8]]$MPD
## [1] 12637.32
## 
## [[8]]$APD
## [1] 34938.64
## 
## [[8]]$D
## [1] 0
## 
## 
## [[9]]
## [[9]]$P
## [1] 35370.47
## 
## [[9]]$MPD
## [1] 2801.658
## 
## [[9]]$APD
## [1] 35427.99
## 
## [[9]]$D
## [1] 0
## 
## 
## [[10]]
## [[10]]$P
## [1] 30843.99
## 
## [[10]]$MPD
## [1] 0
## 
## [[10]]$APD
## [1] 11693.52
## 
## [[10]]$D
## [1] 0
## 
## 
## [[11]]
## [[11]]$P
## [1] 30843.99
## 
## [[11]]$MPD
## [1] 0
## 
## [[11]]$APD
## [1] 11693.52
## 
## [[11]]$D
## [1] 0
## 
## 
## [[12]]
## [[12]]$P
## [1] 30843.99
## 
## [[12]]$MPD
## [1] 0
## 
## [[12]]$APD
## [1] 11693.52
## 
## [[12]]$D
## [1] 0
## 
## 
## [[13]]
## [[13]]$P
## [1] 30843.99
## 
## [[13]]$MPD
## [1] 0
## 
## [[13]]$APD
## [1] 11693.52
## 
## [[13]]$D
## [1] 0
## 
## 
## [[14]]
## [[14]]$P
## [1] 30843.99
## 
## [[14]]$MPD
## [1] 0
## 
## [[14]]$APD
## [1] 11693.52
## 
## [[14]]$D
## [1] 0
## 
## 
## [[15]]
## [[15]]$P
## [1] 30843.99
## 
## [[15]]$MPD
## [1] 0
## 
## [[15]]$APD
## [1] 11693.52
## 
## [[15]]$D
## [1] 0

The microsimulation function for male patients is:

m.M <- m.C <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_males
#Males
Probs <- function(state){
  return(transition_prob_m_alt[[state]])
}
Costs <- function(state) {
  return(transition_costs_m[[state]])
}

# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_m_altB <- transition_prob_m_altB %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altB <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M_altB[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_m_altB[[10]][[current_state]]), 1, prob = transition_prob_m_altB[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_m_altB[[t]][[current_state]]), 1, prob = transition_prob_m_altB[[t]][[current_state]])
         }
        m.M_altB[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M_altB)
}

# Init m.M #repeat it!!!!
model_results_m_altB <- list()
for(i in 1:n.sim) {
m.M_altB <- m.C_altB <-  matrix(nrow = n_males,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M_altB[, 1] <- v.M_1_males
# Microsim loop
model_results_m_altB[[i]] <- loop_microsim_altB(n.t)
print(i)
} 
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15
## [1] 16
## [1] 17
## [1] 18
## [1] 19
## [1] 20
## [1] 21
## [1] 22
## [1] 23
## [1] 24
## [1] 25
## [1] 26
## [1] 27
## [1] 28
## [1] 29
## [1] 30
## [1] 31
## [1] 32
## [1] 33
## [1] 34
## [1] 35
## [1] 36
## [1] 37
## [1] 38
## [1] 39
## [1] 40
## [1] 41
## [1] 42
## [1] 43
## [1] 44
## [1] 45
## [1] 46
## [1] 47
## [1] 48
## [1] 49
## [1] 50
## [1] 51
## [1] 52
## [1] 53
## [1] 54
## [1] 55
## [1] 56
## [1] 57
## [1] 58
## [1] 59
## [1] 60
## [1] 61
## [1] 62
## [1] 63
## [1] 64
## [1] 65
## [1] 66
## [1] 67
## [1] 68
## [1] 69
## [1] 70
## [1] 71
## [1] 72
## [1] 73
## [1] 74
## [1] 75
## [1] 76
## [1] 77
## [1] 78
## [1] 79
## [1] 80
## [1] 81
## [1] 82
## [1] 83
## [1] 84
## [1] 85
## [1] 86
## [1] 87
## [1] 88
## [1] 89
## [1] 90
## [1] 91
## [1] 92
## [1] 93
## [1] 94
## [1] 95
## [1] 96
## [1] 97
## [1] 98
## [1] 99
## [1] 100
# repeat it!!!


#Results of the median simulation, the 50th
model_results_m_altB[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 2   "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 3   "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 5   "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"  
## ind 7   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 8   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 9   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 11  "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 15  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 17  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 18  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 19  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 22  "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 26  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 30  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 31  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 32  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 33  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 38  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 43  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 48  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 53  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 55  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 60  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 61  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 64  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 65  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 66  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 70  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 71  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 72  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 74  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 76  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 80  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 81  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 82  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 85  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 87  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 90  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 92  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 94  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 96  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 97  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 98  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 101 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 102 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 104 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 105 "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 107 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 108 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 112 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 116 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 117 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 120 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 124 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 125 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 128 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 129 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 132 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 133 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 135 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 137 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 139 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 141 "P"     "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 143 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 145 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 152 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 155 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 156 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 164 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 165 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 166 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 168 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 173 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 174 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 178 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 179 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 182 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 188 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 190 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 196 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 197 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 198 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 203 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 204 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 206 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 207 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 208 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 209 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 211 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 213 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 214 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 217 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 220 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 224 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 228 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 230 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 233 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 235 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 238 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 239 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 241 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 243 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 244 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 250 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 255 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 258 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 259 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 267 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 268 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 269 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 270 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 272 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 273 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 278 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 279 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 281 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 287 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 294 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 296 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 299 "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 2   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 20  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 21  "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 22  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 23  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 24  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 25  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 26  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 27  "MPD"   "MPD"    "MPD"    "MPD"    "D"      "D"      "D"     
## ind 28  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 29  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 30  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 31  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 32  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 33  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 34  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 35  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 36  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 37  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 38  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 39  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 40  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 41  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 42  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 43  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 44  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 45  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 46  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 47  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 48  "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 49  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 50  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 51  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 52  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 53  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 54  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 55  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 56  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 57  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 58  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 59  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 60  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 61  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 65  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 68  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 72  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 73  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 74  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 75  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 86  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 87  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 88  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 93  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 94  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
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## ind 100 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 102 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 103 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 104 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
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## ind 106 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 109 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 110 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
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## ind 112 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 113 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 114 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 115 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 116 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 134 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
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## ind 140 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
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## ind 144 "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
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## ind 166 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 173 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
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## ind 183 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
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## ind 203 "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
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## ind 268 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 269 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 270 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 271 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 272 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 273 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 274 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 275 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 276 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 277 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 278 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 279 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 280 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 281 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 282 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 283 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 284 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 285 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 286 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 287 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 288 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 289 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 290 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 291 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 292 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 293 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 294 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 295 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 296 "APD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 297 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 298 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 299 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 300 "D"     "D"      "D"      "D"      "D"      "D"      "D"
df_m.M_altB <- model_results_m_altB[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M_altB %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 15600       1
## 
## [[2]]
##  cycle 1     n    percent
##        D   475 0.03044872
##      MPD 15125 0.96955128
## 
## [[3]]
##  cycle 2     n    percent
##      APD   653 0.04185897
##        D   985 0.06314103
##      MPD 13962 0.89500000
## 
## [[4]]
##  cycle 3     n   percent
##      APD  1015 0.0650641
##        D  1939 0.1242949
##      MPD 12646 0.8106410
## 
## [[5]]
##  cycle 4     n    percent
##      APD  1157 0.07416667
##        D  3451 0.22121795
##      MPD 10992 0.70461538
## 
## [[6]]
##  cycle 5    n    percent
##      APD  983 0.06301282
##        D 5413 0.34698718
##      MPD 9204 0.59000000
## 
## [[7]]
##  cycle 6    n   percent
##      APD  740 0.0474359
##        D 7590 0.4865385
##      MPD 7270 0.4660256
## 
## [[8]]
##  cycle 7    n    percent
##      APD  395 0.02532051
##        D 9989 0.64032051
##      MPD 5216 0.33435897
## 
## [[9]]
##  cycle 8     n    percent
##      APD   152 0.00974359
##        D 12334 0.79064103
##      MPD  3114 0.19961538
## 
## [[10]]
##  cycle 9     n     percent
##      APD    49 0.003141026
##        D 14003 0.897628205
##      MPD  1548 0.099230769
## 
## [[11]]
##  cycle 10     n      percent
##       APD     4 0.0002564103
##         D 14975 0.9599358974
##       MPD   621 0.0398076923
## 
## [[12]]
##  cycle 11     n      percent
##       APD     2 0.0001282051
##         D 15381 0.9859615385
##       MPD   217 0.0139102564
## 
## [[13]]
##  cycle 12     n     percent
##         D 15516 0.994615385
##       MPD    84 0.005384615
## 
## [[14]]
##  cycle 13     n     percent
##         D 15567 0.997884615
##       MPD    33 0.002115385
## 
## [[15]]
##  cycle 14     n      percent
##         D 15589 0.9992948718
##       MPD    11 0.0007051282
# Transition costs in a dataframe
transition_costs_m_alt <-
 transition_costs_m_alt %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
   "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")


final_cost_m_altB <-
  map(
    model_results_m_altB,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_m_alt
      )
  )
  

final_cost_m2_altB <-
  map(
    final_cost_m_altB,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
     filter(cycle != "cycle 15")
  )
final_cost_m2_altB
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 242802784.
##  4 cycle 3  15600 287852992.
##  5 cycle 4  15600 262537326.
##  6 cycle 5  15600 202170128.
##  7 cycle 6  15600 136584230.
##  8 cycle 7  15600  55870690.
##  9 cycle 8  15600  19554881.
## 10 cycle 9  15600   9488727.
## 11 cycle 10 15600   3940396.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    578348.
## 14 cycle 13 15600    184309.
## 15 cycle 14 15600     76266.
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 242001502.
##  4 cycle 3  15600 285783516.
##  5 cycle 4  15600 260836866.
##  6 cycle 5  15600 201313783.
##  7 cycle 6  15600 136470175.
##  8 cycle 7  15600  57679233.
##  9 cycle 8  15600  20081277.
## 10 cycle 9  15600   9673036.
## 11 cycle 10 15600   3851419.
## 12 cycle 11 15600   1499893.
## 13 cycle 12 15600    571993.
## 14 cycle 13 15600    184309.
## 15 cycle 14 15600     50844.
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 242121539.
##  4 cycle 3  15600 285147692.
##  5 cycle 4  15600 262306202.
##  6 cycle 5  15600 202025303.
##  7 cycle 6  15600 136305588.
##  8 cycle 7  15600  56636056.
##  9 cycle 8  15600  20431622.
## 10 cycle 9  15600  10079786.
## 11 cycle 10 15600   3902263.
## 12 cycle 11 15600   1677846.
## 13 cycle 12 15600    660970.
## 14 cycle 13 15600    241508.
## 15 cycle 14 15600     88977.
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285122707.
##  3 cycle 2  15600 241612360.
##  4 cycle 3  15600 285680272.
##  5 cycle 4  15600 261806088.
##  6 cycle 5  15600 201973826.
##  7 cycle 6  15600 135673833.
##  8 cycle 7  15600  56956721.
##  9 cycle 8  15600  19961204.
## 10 cycle 9  15600   9978098.
## 11 cycle 10 15600   3864130.
## 12 cycle 11 15600   1563447.
## 13 cycle 12 15600    622837.
## 14 cycle 13 15600    216086.
## 15 cycle 14 15600     82621.
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 244062850.
##  4 cycle 3  15600 284758861.
##  5 cycle 4  15600 258922378.
##  6 cycle 5  15600 200173447.
##  7 cycle 6  15600 134255089.
##  8 cycle 7  15600  56622367.
##  9 cycle 8  15600  19785540.
## 10 cycle 9  15600  10136985.
## 11 cycle 10 15600   3698887.
## 12 cycle 11 15600   1525314.
## 13 cycle 12 15600    660970.
## 14 cycle 13 15600    216086.
## 15 cycle 14 15600    101688.
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285085097.
##  3 cycle 2  15600 243228460.
##  4 cycle 3  15600 286550912.
##  5 cycle 4  15600 264160300.
##  6 cycle 5  15600 203798356.
##  7 cycle 6  15600 136954544.
##  8 cycle 7  15600  57227472.
##  9 cycle 8  15600  20160711.
## 10 cycle 9  15600   9959032.
## 11 cycle 10 15600   3864130.
## 12 cycle 11 15600   1588869.
## 13 cycle 12 15600    584704.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600     82621.
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285799690.
##  3 cycle 2  15600 243654136.
##  4 cycle 3  15600 287141202.
##  5 cycle 4  15600 263491594.
##  6 cycle 5  15600 202014479.
##  7 cycle 6  15600 136150904.
##  8 cycle 7  15600  57431768.
##  9 cycle 8  15600  20323514.
## 10 cycle 9  15600   9456949.
## 11 cycle 10 15600   3692532.
## 12 cycle 11 15600   1385494.
## 13 cycle 12 15600    552926.
## 14 cycle 13 15600    273286.
## 15 cycle 14 15600     88977.
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 242152691.
##  4 cycle 3  15600 284482488.
##  5 cycle 4  15600 259657712.
##  6 cycle 5  15600 201837234.
##  7 cycle 6  15600 136030590.
##  8 cycle 7  15600  56833146.
##  9 cycle 8  15600  20157636.
## 10 cycle 9  15600   9469660.
## 11 cycle 10 15600   3895907.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    502083.
## 14 cycle 13 15600    139820.
## 15 cycle 14 15600     57199.
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 240566110.
##  4 cycle 3  15600 282940985.
##  5 cycle 4  15600 260072433.
##  6 cycle 5  15600 200794741.
##  7 cycle 6  15600 136254536.
##  8 cycle 7  15600  57821597.
##  9 cycle 8  15600  20315907.
## 10 cycle 9  15600   9838278.
## 11 cycle 10 15600   3724309.
## 12 cycle 11 15600   1353717.
## 13 cycle 12 15600    514794.
## 14 cycle 13 15600    190664.
## 15 cycle 14 15600     82621.
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285574029.
##  3 cycle 2  15600 244307002.
##  4 cycle 3  15600 285833254.
##  5 cycle 4  15600 260905922.
##  6 cycle 5  15600 202219033.
##  7 cycle 6  15600 136026937.
##  8 cycle 7  15600  56607929.
##  9 cycle 8  15600  19752596.
## 10 cycle 9  15600   9577703.
## 11 cycle 10 15600   3489157.
## 12 cycle 11 15600   1321939.
## 13 cycle 12 15600    495727.
## 14 cycle 13 15600    158887.
## 15 cycle 14 15600     44488.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 244174080.
##  4 cycle 3  15600 287482626.
##  5 cycle 4  15600 263323760.
##  6 cycle 5  15600 203153512.
##  7 cycle 6  15600 137776393.
##  8 cycle 7  15600  57136349.
##  9 cycle 8  15600  19852287.
## 10 cycle 9  15600   9164597.
## 11 cycle 10 15600   3590844.
## 12 cycle 11 15600   1379138.
## 13 cycle 12 15600    463950.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600     95332.
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 244565343.
##  4 cycle 3  15600 286645764.
##  5 cycle 4  15600 261690501.
##  6 cycle 5  15600 202713289.
##  7 cycle 6  15600 136033196.
##  8 cycle 7  15600  56889660.
##  9 cycle 8  15600  20029445.
## 10 cycle 9  15600   9361617.
## 11 cycle 10 15600   3679821.
## 12 cycle 11 15600   1410916.
## 13 cycle 12 15600    616481.
## 14 cycle 13 15600    292352.
## 15 cycle 14 15600    120754.
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284314088.
##  3 cycle 2  15600 242652247.
##  4 cycle 3  15600 285920555.
##  5 cycle 4  15600 262266717.
##  6 cycle 5  15600 199911704.
##  7 cycle 6  15600 134386863.
##  8 cycle 7  15600  56142796.
##  9 cycle 8  15600  19745673.
## 10 cycle 9  15600   9444238.
## 11 cycle 10 15600   3520934.
## 12 cycle 11 15600   1372783.
## 13 cycle 12 15600    489372.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600     88977.
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285461198.
##  3 cycle 2  15600 243585962.
##  4 cycle 3  15600 287244446.
##  5 cycle 4  15600 259283520.
##  6 cycle 5  15600 200265577.
##  7 cycle 6  15600 134476449.
##  8 cycle 7  15600  54977399.
##  9 cycle 8  15600  19137577.
## 10 cycle 9  15600   9170953.
## 11 cycle 10 15600   3501868.
## 12 cycle 11 15600   1277451.
## 13 cycle 12 15600    432172.
## 14 cycle 13 15600    171598.
## 15 cycle 14 15600     50844.
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 243529857.
##  4 cycle 3  15600 284112333.
##  5 cycle 4  15600 260771857.
##  6 cycle 5  15600 200787124.
##  7 cycle 6  15600 134253522.
##  8 cycle 7  15600  55670286.
##  9 cycle 8  15600  19833611.
## 10 cycle 9  15600   9450594.
## 11 cycle 10 15600   3660755.
## 12 cycle 11 15600   1480826.
## 13 cycle 12 15600    521149.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600    101688.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 242912057.
##  4 cycle 3  15600 285568427.
##  5 cycle 4  15600 261795467.
##  6 cycle 5  15600 202092630.
##  7 cycle 6  15600 135021752.
##  8 cycle 7  15600  56877787.
##  9 cycle 8  15600  19819256.
## 10 cycle 9  15600   9495082.
## 11 cycle 10 15600   3838708.
## 12 cycle 11 15600   1232962.
## 13 cycle 12 15600    444883.
## 14 cycle 13 15600    127110.
## 15 cycle 14 15600     57199.
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 243358609.
##  4 cycle 3  15600 284158076.
##  5 cycle 4  15600 258739640.
##  6 cycle 5  15600 200574303.
##  7 cycle 6  15600 135208212.
##  8 cycle 7  15600  56632886.
##  9 cycle 8  15600  19865360.
## 10 cycle 9  15600   9399750.
## 11 cycle 10 15600   3749731.
## 12 cycle 11 15600   1360072.
## 13 cycle 12 15600    527505.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     88977.
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 241213594.
##  4 cycle 3  15600 283569468.
##  5 cycle 4  15600 258967527.
##  6 cycle 5  15600 198625257.
##  7 cycle 6  15600 133104065.
##  8 cycle 7  15600  56308618.
##  9 cycle 8  15600  19801651.
## 10 cycle 9  15600   9304418.
## 11 cycle 10 15600   3673466.
## 12 cycle 11 15600   1518959.
## 13 cycle 12 15600    578348.
## 14 cycle 13 15600    247864.
## 15 cycle 14 15600     63555.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 244406815.
##  4 cycle 3  15600 286376119.
##  5 cycle 4  15600 261908053.
##  6 cycle 5  15600 200554611.
##  7 cycle 6  15600 134639988.
##  8 cycle 7  15600  56625220.
##  9 cycle 8  15600  20096018.
## 10 cycle 9  15600   9507793.
## 11 cycle 10 15600   3908618.
## 12 cycle 11 15600   1569803.
## 13 cycle 12 15600    546571.
## 14 cycle 13 15600    266930.
## 15 cycle 14 15600     82621.
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284803020.
##  3 cycle 2  15600 241996773.
##  4 cycle 3  15600 283409987.
##  5 cycle 4  15600 260074338.
##  6 cycle 5  15600 198444237.
##  7 cycle 6  15600 134387391.
##  8 cycle 7  15600  57570990.
##  9 cycle 8  15600  20661981.
## 10 cycle 9  15600  10041653.
## 11 cycle 10 15600   3908618.
## 12 cycle 11 15600   1442693.
## 13 cycle 12 15600    559282.
## 14 cycle 13 15600    197020.
## 15 cycle 14 15600     69910.
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284276478.
##  3 cycle 2  15600 241780183.
##  4 cycle 3  15600 283250295.
##  5 cycle 4  15600 259399917.
##  6 cycle 5  15600 201214654.
##  7 cycle 6  15600 136494644.
##  8 cycle 7  15600  56171356.
##  9 cycle 8  15600  19457456.
## 10 cycle 9  15600   9984454.
## 11 cycle 10 15600   3825997.
## 12 cycle 11 15600   1665135.
## 13 cycle 12 15600    622837.
## 14 cycle 13 15600    235153.
## 15 cycle 14 15600     95332.
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 241738431.
##  4 cycle 3  15600 284477040.
##  5 cycle 4  15600 261430801.
##  6 cycle 5  15600 202166337.
##  7 cycle 6  15600 136097785.
##  8 cycle 7  15600  56527063.
##  9 cycle 8  15600  20406410.
## 10 cycle 9  15600   9895477.
## 11 cycle 10 15600   3806931.
## 12 cycle 11 15600   1544381.
## 13 cycle 12 15600    635548.
## 14 cycle 13 15600    266930.
## 15 cycle 14 15600    120754.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 241634540.
##  4 cycle 3  15600 285204140.
##  5 cycle 4  15600 262287151.
##  6 cycle 5  15600 202455388.
##  7 cycle 6  15600 135635808.
##  8 cycle 7  15600  57268050.
##  9 cycle 8  15600  20296509.
## 10 cycle 9  15600   9825567.
## 11 cycle 10 15600   3857774.
## 12 cycle 11 15600   1525314.
## 13 cycle 12 15600    673681.
## 14 cycle 13 15600    260575.
## 15 cycle 14 15600     69910.
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284746605.
##  3 cycle 2  15600 243307886.
##  4 cycle 3  15600 284019163.
##  5 cycle 4  15600 261614348.
##  6 cycle 5  15600 202158704.
##  7 cycle 6  15600 135120712.
##  8 cycle 7  15600  56838997.
##  9 cycle 8  15600  20065465.
## 10 cycle 9  15600   9889122.
## 11 cycle 10 15600   4181904.
## 12 cycle 11 15600   1652424.
## 13 cycle 12 15600    552926.
## 14 cycle 13 15600    241508.
## 15 cycle 14 15600     76266.
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 242928855.
##  4 cycle 3  15600 285784988.
##  5 cycle 4  15600 263377103.
##  6 cycle 5  15600 202511274.
##  7 cycle 6  15600 134896747.
##  8 cycle 7  15600  55948559.
##  9 cycle 8  15600  19946463.
## 10 cycle 9  15600   9526860.
## 11 cycle 10 15600   3794220.
## 12 cycle 11 15600   1525314.
## 13 cycle 12 15600    565637.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600    108043.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 243420096.
##  4 cycle 3  15600 287094618.
##  5 cycle 4  15600 264057762.
##  6 cycle 5  15600 202638946.
##  7 cycle 6  15600 136484751.
##  8 cycle 7  15600  56355074.
##  9 cycle 8  15600  20058330.
## 10 cycle 9  15600   9800145.
## 11 cycle 10 15600   3857774.
## 12 cycle 11 15600   1455404.
## 13 cycle 12 15600    616481.
## 14 cycle 13 15600    330485.
## 15 cycle 14 15600    127110.
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 241496888.
##  4 cycle 3  15600 284420171.
##  5 cycle 4  15600 258637102.
##  6 cycle 5  15600 198902248.
##  7 cycle 6  15600 133029074.
##  8 cycle 7  15600  55182155.
##  9 cycle 8  15600  19391867.
## 10 cycle 9  15600   9101043.
## 11 cycle 10 15600   3495512.
## 12 cycle 11 15600   1398205.
## 13 cycle 12 15600    508438.
## 14 cycle 13 15600    177953.
## 15 cycle 14 15600     69910.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284878241.
##  3 cycle 2  15600 242364387.
##  4 cycle 3  15600 286499720.
##  5 cycle 4  15600 258334968.
##  6 cycle 5  15600 198615736.
##  7 cycle 6  15600 134081138.
##  8 cycle 7  15600  55823630.
##  9 cycle 8  15600  20195025.
## 10 cycle 9  15600   9660325.
## 11 cycle 10 15600   3927685.
## 12 cycle 11 15600   1474471.
## 13 cycle 12 15600    635548.
## 14 cycle 13 15600    279641.
## 15 cycle 14 15600     76266.
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 242430604.
##  4 cycle 3  15600 285747425.
##  5 cycle 4  15600 260863251.
##  6 cycle 5  15600 202118685.
##  7 cycle 6  15600 134973835.
##  8 cycle 7  15600  57221910.
##  9 cycle 8  15600  20108880.
## 10 cycle 9  15600   9609481.
## 11 cycle 10 15600   3851419.
## 12 cycle 11 15600   1296517.
## 13 cycle 12 15600    514794.
## 14 cycle 13 15600    184309.
## 15 cycle 14 15600     57199.
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 245916903.
##  4 cycle 3  15600 288120973.
##  5 cycle 4  15600 262140796.
##  6 cycle 5  15600 199388837.
##  7 cycle 6  15600 132083757.
##  8 cycle 7  15600  54887485.
##  9 cycle 8  15600  19299484.
## 10 cycle 9  15600   8910378.
## 11 cycle 10 15600   3374758.
## 12 cycle 11 15600   1290162.
## 13 cycle 12 15600    521149.
## 14 cycle 13 15600    171598.
## 15 cycle 14 15600     82621.
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 243374103.
##  4 cycle 3  15600 284666322.
##  5 cycle 4  15600 260661698.
##  6 cycle 5  15600 199741441.
##  7 cycle 6  15600 134929561.
##  8 cycle 7  15600  56181730.
##  9 cycle 8  15600  19836773.
## 10 cycle 9  15600   9539571.
## 11 cycle 10 15600   3762442.
## 12 cycle 11 15600   1499893.
## 13 cycle 12 15600    578348.
## 14 cycle 13 15600    197020.
## 15 cycle 14 15600     88977.
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 240734750.
##  4 cycle 3  15600 285354179.
##  5 cycle 4  15600 258883231.
##  6 cycle 5  15600 200238887.
##  7 cycle 6  15600 134691030.
##  8 cycle 7  15600  57155915.
##  9 cycle 8  15600  19910269.
## 10 cycle 9  15600   9393395.
## 11 cycle 10 15600   3698887.
## 12 cycle 11 15600   1404560.
## 13 cycle 12 15600    540216.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     82621.
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 242489154.
##  4 cycle 3  15600 285637472.
##  5 cycle 4  15600 262083457.
##  6 cycle 5  15600 202769826.
##  7 cycle 6  15600 134114470.
##  8 cycle 7  15600  56557871.
##  9 cycle 8  15600  19736572.
## 10 cycle 9  15600   9450594.
## 11 cycle 10 15600   3673466.
## 12 cycle 11 15600   1366428.
## 13 cycle 12 15600    514794.
## 14 cycle 13 15600    260575.
## 15 cycle 14 15600    133465.
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 243985217.
##  4 cycle 3  15600 284832093.
##  5 cycle 4  15600 261369889.
##  6 cycle 5  15600 200196329.
##  7 cycle 6  15600 133193642.
##  8 cycle 7  15600  55741698.
##  9 cycle 8  15600  19116299.
## 10 cycle 9  15600   9081976.
## 11 cycle 10 15600   3438313.
## 12 cycle 11 15600   1334650.
## 13 cycle 12 15600    527505.
## 14 cycle 13 15600    235153.
## 15 cycle 14 15600    101688.
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 242547216.
##  4 cycle 3  15600 284538936.
##  5 cycle 4  15600 260726472.
##  6 cycle 5  15600 202566542.
##  7 cycle 6  15600 133800938.
##  8 cycle 7  15600  56773002.
##  9 cycle 8  15600  20265744.
## 10 cycle 9  15600   9685747.
## 11 cycle 10 15600   3540001.
## 12 cycle 11 15600   1391849.
## 13 cycle 12 15600    571993.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600     88977.
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 242125617.
##  4 cycle 3  15600 285815840.
##  5 cycle 4  15600 261323070.
##  6 cycle 5  15600 198737062.
##  7 cycle 6  15600 133679567.
##  8 cycle 7  15600  55250543.
##  9 cycle 8  15600  19456348.
## 10 cycle 9  15600   9393395.
## 11 cycle 10 15600   3628977.
## 12 cycle 11 15600   1328295.
## 13 cycle 12 15600    527505.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600     95332.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 241450243.
##  4 cycle 3  15600 283401385.
##  5 cycle 4  15600 260797146.
##  6 cycle 5  15600 201049485.
##  7 cycle 6  15600 134153533.
##  8 cycle 7  15600  55520861.
##  9 cycle 8  15600  19610772.
## 10 cycle 9  15600   9755657.
## 11 cycle 10 15600   3609911.
## 12 cycle 11 15600   1417271.
## 13 cycle 12 15600    559282.
## 14 cycle 13 15600    216086.
## 15 cycle 14 15600     76266.
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 243453693.
##  4 cycle 3  15600 286086536.
##  5 cycle 4  15600 260504823.
##  6 cycle 5  15600 201131443.
##  7 cycle 6  15600 136114956.
##  8 cycle 7  15600  56993091.
##  9 cycle 8  15600  20025000.
## 10 cycle 9  15600   9596770.
## 11 cycle 10 15600   3730665.
## 12 cycle 11 15600   1461760.
## 13 cycle 12 15600    641903.
## 14 cycle 13 15600    324129.
## 15 cycle 14 15600    120754.
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285555224.
##  3 cycle 2  15600 243237267.
##  4 cycle 3  15600 287395325.
##  5 cycle 4  15600 262544946.
##  6 cycle 5  15600 200813163.
##  7 cycle 6  15600 134323831.
##  8 cycle 7  15600  54518721.
##  9 cycle 8  15600  19279015.
## 10 cycle 9  15600   9272641.
## 11 cycle 10 15600   3698887.
## 12 cycle 11 15600   1442693.
## 13 cycle 12 15600    552926.
## 14 cycle 13 15600    158887.
## 15 cycle 14 15600     82621.
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284238868.
##  3 cycle 2  15600 242750104.
##  4 cycle 3  15600 284935967.
##  5 cycle 4  15600 263317522.
##  6 cycle 5  15600 200307466.
##  7 cycle 6  15600 133935808.
##  8 cycle 7  15600  56321386.
##  9 cycle 8  15600  19805112.
## 10 cycle 9  15600   9240863.
## 11 cycle 10 15600   3628977.
## 12 cycle 11 15600   1417271.
## 13 cycle 12 15600    438528.
## 14 cycle 13 15600    165242.
## 15 cycle 14 15600     63555.
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 243890785.
##  4 cycle 3  15600 284553636.
##  5 cycle 4  15600 260785715.
##  6 cycle 5  15600 201314384.
##  7 cycle 6  15600 134412369.
##  8 cycle 7  15600  56598449.
##  9 cycle 8  15600  20190368.
## 10 cycle 9  15600   9342551.
## 11 cycle 10 15600   3527290.
## 12 cycle 11 15600   1321939.
## 13 cycle 12 15600    533860.
## 14 cycle 13 15600    197020.
## 15 cycle 14 15600     76266.
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285066292.
##  3 cycle 2  15600 242727924.
##  4 cycle 3  15600 284184093.
##  5 cycle 4  15600 262340730.
##  6 cycle 5  15600 202339106.
##  7 cycle 6  15600 133956142.
##  8 cycle 7  15600  54978292.
##  9 cycle 8  15600  19456858.
## 10 cycle 9  15600   9164597.
## 11 cycle 10 15600   3457379.
## 12 cycle 11 15600   1302873.
## 13 cycle 12 15600    622837.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600     76266.
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 243886055.
##  4 cycle 3  15600 288378442.
##  5 cycle 4  15600 262057882.
##  6 cycle 5  15600 200735030.
##  7 cycle 6  15600 135755593.
##  8 cycle 7  15600  56318849.
##  9 cycle 8  15600  20224893.
## 10 cycle 9  15600   9622192.
## 11 cycle 10 15600   3864130.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    641903.
## 14 cycle 13 15600    241508.
## 15 cycle 14 15600    127110.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 242524219.
##  4 cycle 3  15600 284560976.
##  5 cycle 4  15600 260685369.
##  6 cycle 5  15600 201315018.
##  7 cycle 6  15600 133634265.
##  8 cycle 7  15600  55063538.
##  9 cycle 8  15600  19101172.
## 10 cycle 9  15600   9387039.
## 11 cycle 10 15600   3737020.
## 12 cycle 11 15600   1461760.
## 13 cycle 12 15600    616481.
## 14 cycle 13 15600    165242.
## 15 cycle 14 15600     44488.
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 241536683.
##  4 cycle 3  15600 285705677.
##  5 cycle 4  15600 258228097.
##  6 cycle 5  15600 199139839.
##  7 cycle 6  15600 134254041.
##  8 cycle 7  15600  55156133.
##  9 cycle 8  15600  19461988.
## 10 cycle 9  15600   9348906.
## 11 cycle 10 15600   3590844.
## 12 cycle 11 15600   1341006.
## 13 cycle 12 15600    527505.
## 14 cycle 13 15600    152531.
## 15 cycle 14 15600     69910.
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284539749.
##  3 cycle 2  15600 242827737.
##  4 cycle 3  15600 284327422.
##  5 cycle 4  15600 261245872.
##  6 cycle 5  15600 200176637.
##  7 cycle 6  15600 133971775.
##  8 cycle 7  15600  56932513.
##  9 cycle 8  15600  19954580.
## 10 cycle 9  15600   9609481.
## 11 cycle 10 15600   3825997.
## 12 cycle 11 15600   1582514.
## 13 cycle 12 15600    616481.
## 14 cycle 13 15600    260575.
## 15 cycle 14 15600    120754.
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284690190.
##  3 cycle 2  15600 244182070.
##  4 cycle 3  15600 286183070.
##  5 cycle 4  15600 259338718.
##  6 cycle 5  15600 201288362.
##  7 cycle 6  15600 134443106.
##  8 cycle 7  15600  56554557.
##  9 cycle 8  15600  19828357.
## 10 cycle 9  15600   9469660.
## 11 cycle 10 15600   3768798.
## 12 cycle 11 15600   1360072.
## 13 cycle 12 15600    571993.
## 14 cycle 13 15600    184309.
## 15 cycle 14 15600     76266.
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 242110775.
##  4 cycle 3  15600 284664430.
##  5 cycle 4  15600 260207071.
##  6 cycle 5  15600 199271388.
##  7 cycle 6  15600 134618644.
##  8 cycle 7  15600  56727901.
##  9 cycle 8  15600  20039443.
## 10 cycle 9  15600  10016231.
## 11 cycle 10 15600   3667110.
## 12 cycle 11 15600   1474471.
## 13 cycle 12 15600    546571.
## 14 cycle 13 15600    197020.
## 15 cycle 14 15600     82621.
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 243910356.
##  4 cycle 3  15600 285936918.
##  5 cycle 4  15600 261339407.
##  6 cycle 5  15600 200250294.
##  7 cycle 6  15600 134935281.
##  8 cycle 7  15600  56358216.
##  9 cycle 8  15600  20108581.
## 10 cycle 9  15600   9850989.
## 11 cycle 10 15600   3959462.
## 12 cycle 11 15600   1518959.
## 13 cycle 12 15600    629192.
## 14 cycle 13 15600    241508.
## 15 cycle 14 15600     76266.
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284426919.
##  3 cycle 2  15600 242895911.
##  4 cycle 3  15600 283673132.
##  5 cycle 4  15600 259431730.
##  6 cycle 5  15600 199315815.
##  7 cycle 6  15600 134472795.
##  8 cycle 7  15600  56372510.
##  9 cycle 8  15600  19594449.
## 10 cycle 9  15600   9838278.
## 11 cycle 10 15600   3946751.
## 12 cycle 11 15600   1379138.
## 13 cycle 12 15600    533860.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     69910.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285216732.
##  3 cycle 2  15600 242919560.
##  4 cycle 3  15600 284785298.
##  5 cycle 4  15600 259382248.
##  6 cycle 5  15600 200609261.
##  7 cycle 6  15600 135285290.
##  8 cycle 7  15600  58298604.
##  9 cycle 8  15600  20227371.
## 10 cycle 9  15600   9406106.
## 11 cycle 10 15600   3559067.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    546571.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600     95332.
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 242519489.
##  4 cycle 3  15600 285906907.
##  5 cycle 4  15600 263513882.
##  6 cycle 5  15600 202736789.
##  7 cycle 6  15600 136802965.
##  8 cycle 7  15600  56970698.
##  9 cycle 8  15600  20315695.
## 10 cycle 9  15600   9539571.
## 11 cycle 10 15600   3705243.
## 12 cycle 11 15600   1372783.
## 13 cycle 12 15600    591059.
## 14 cycle 13 15600    177953.
## 15 cycle 14 15600     76266.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 241480090.
##  4 cycle 3  15600 283949907.
##  5 cycle 4  15600 261125143.
##  6 cycle 5  15600 201083808.
##  7 cycle 6  15600 135350917.
##  8 cycle 7  15600  56389340.
##  9 cycle 8  15600  19846472.
## 10 cycle 9  15600   9507793.
## 11 cycle 10 15600   3730665.
## 12 cycle 11 15600   1538025.
## 13 cycle 12 15600    667325.
## 14 cycle 13 15600    247864.
## 15 cycle 14 15600     88977.
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284897046.
##  3 cycle 2  15600 241385006.
##  4 cycle 3  15600 283013166.
##  5 cycle 4  15600 260655224.
##  6 cycle 5  15600 201483412.
##  7 cycle 6  15600 134216565.
##  8 cycle 7  15600  56569600.
##  9 cycle 8  15600  19587228.
## 10 cycle 9  15600   9711168.
## 11 cycle 10 15600   3806931.
## 12 cycle 11 15600   1557092.
## 13 cycle 12 15600    476661.
## 14 cycle 13 15600    184309.
## 15 cycle 14 15600     63555.
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284727800.
##  3 cycle 2  15600 243578459.
##  4 cycle 3  15600 285168471.
##  5 cycle 4  15600 257812853.
##  6 cycle 5  15600 200008895.
##  7 cycle 6  15600 135293616.
##  8 cycle 7  15600  55639911.
##  9 cycle 8  15600  19538945.
## 10 cycle 9  15600   9247219.
## 11 cycle 10 15600   3667110.
## 12 cycle 11 15600   1487182.
## 13 cycle 12 15600    508438.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     88977.
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285592834.
##  3 cycle 2  15600 244282048.
##  4 cycle 3  15600 287449040.
##  5 cycle 4  15600 262493559.
##  6 cycle 5  15600 202999183.
##  7 cycle 6  15600 136559242.
##  8 cycle 7  15600  56297034.
##  9 cycle 8  15600  19135187.
## 10 cycle 9  15600   9431527.
## 11 cycle 10 15600   3832352.
## 12 cycle 11 15600   1455404.
## 13 cycle 12 15600    552926.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600     57199.
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284859436.
##  3 cycle 2  15600 244233446.
##  4 cycle 3  15600 286423755.
##  5 cycle 4  15600 261129189.
##  6 cycle 5  15600 202492182.
##  7 cycle 6  15600 135492055.
##  8 cycle 7  15600  55379074.
##  9 cycle 8  15600  20101447.
## 10 cycle 9  15600   9259930.
## 11 cycle 10 15600   3603555.
## 12 cycle 11 15600   1360072.
## 13 cycle 12 15600    470305.
## 14 cycle 13 15600    190664.
## 15 cycle 14 15600     82621.
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285310758.
##  3 cycle 2  15600 243794397.
##  4 cycle 3  15600 286591839.
##  5 cycle 4  15600 262528896.
##  6 cycle 5  15600 203512479.
##  7 cycle 6  15600 136179026.
##  8 cycle 7  15600  56432193.
##  9 cycle 8  15600  20173958.
## 10 cycle 9  15600   9857344.
## 11 cycle 10 15600   3972173.
## 12 cycle 11 15600   1607936.
## 13 cycle 12 15600    616481.
## 14 cycle 13 15600    247864.
## 15 cycle 14 15600     82621.
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 240263897.
##  4 cycle 3  15600 282868594.
##  5 cycle 4  15600 256806911.
##  6 cycle 5  15600 198104327.
##  7 cycle 6  15600 132021255.
##  8 cycle 7  15600  54930194.
##  9 cycle 8  15600  19652991.
## 10 cycle 9  15600   9228152.
## 11 cycle 10 15600   3552712.
## 12 cycle 11 15600   1372783.
## 13 cycle 12 15600    527505.
## 14 cycle 13 15600    216086.
## 15 cycle 14 15600    101688.
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 243092112.
##  4 cycle 3  15600 282568097.
##  5 cycle 4  15600 256332660.
##  6 cycle 5  15600 198297457.
##  7 cycle 6  15600 133392081.
##  8 cycle 7  15600  56084612.
##  9 cycle 8  15600  20036069.
## 10 cycle 9  15600   9768368.
## 11 cycle 10 15600   3876841.
## 12 cycle 11 15600   1607936.
## 13 cycle 12 15600    641903.
## 14 cycle 13 15600    228797.
## 15 cycle 14 15600    120754.
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284614970.
##  3 cycle 2  15600 241060612.
##  4 cycle 3  15600 286149274.
##  5 cycle 4  15600 261411228.
##  6 cycle 5  15600 202229222.
##  7 cycle 6  15600 135168630.
##  8 cycle 7  15600  56537582.
##  9 cycle 8  15600  19564070.
## 10 cycle 9  15600   9476016.
## 11 cycle 10 15600   3851419.
## 12 cycle 11 15600   1493537.
## 13 cycle 12 15600    610126.
## 14 cycle 13 15600    279641.
## 15 cycle 14 15600    108043.
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285329563.
##  3 cycle 2  15600 241902993.
##  4 cycle 3  15600 286431306.
##  5 cycle 4  15600 261573009.
##  6 cycle 5  15600 200224890.
##  7 cycle 6  15600 133892072.
##  8 cycle 7  15600  55578729.
##  9 cycle 8  15600  19661196.
## 10 cycle 9  15600   9361617.
## 11 cycle 10 15600   3590844.
## 12 cycle 11 15600   1506248.
## 13 cycle 12 15600    584704.
## 14 cycle 13 15600    247864.
## 15 cycle 14 15600     95332.
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285235537.
##  3 cycle 2  15600 243558888.
##  4 cycle 3  15600 285741347.
##  5 cycle 4  15600 259021629.
##  6 cycle 5  15600 197834351.
##  7 cycle 6  15600 133498328.
##  8 cycle 7  15600  55841670.
##  9 cycle 8  15600  19961889.
## 10 cycle 9  15600   9564993.
## 11 cycle 10 15600   3832352.
## 12 cycle 11 15600   1480826.
## 13 cycle 12 15600    610126.
## 14 cycle 13 15600    305063.
## 15 cycle 14 15600    120754.
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 242429787.
##  4 cycle 3  15600 285713017.
##  5 cycle 4  15600 257919724.
##  6 cycle 5  15600 197246061.
##  7 cycle 6  15600 132208762.
##  8 cycle 7  15600  54887341.
##  9 cycle 8  15600  19292175.
## 10 cycle 9  15600   9240863.
## 11 cycle 10 15600   3387469.
## 12 cycle 11 15600   1309228.
## 13 cycle 12 15600    463950.
## 14 cycle 13 15600    235153.
## 15 cycle 14 15600     88977.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 241385006.
##  4 cycle 3  15600 284642390.
##  5 cycle 4  15600 260869775.
##  6 cycle 5  15600 202087535.
##  7 cycle 6  15600 137005576.
##  8 cycle 7  15600  57019402.
##  9 cycle 8  15600  20337060.
## 10 cycle 9  15600   9437883.
## 11 cycle 10 15600   3495512.
## 12 cycle 11 15600   1404560.
## 13 cycle 12 15600    616481.
## 14 cycle 13 15600    273286.
## 15 cycle 14 15600    114399.
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 242266530.
##  4 cycle 3  15600 283202869.
##  5 cycle 4  15600 257281213.
##  6 cycle 5  15600 199110644.
##  7 cycle 6  15600 133879074.
##  8 cycle 7  15600  56578620.
##  9 cycle 8  15600  19645085.
## 10 cycle 9  15600   9450594.
## 11 cycle 10 15600   3616266.
## 12 cycle 11 15600   1379138.
## 13 cycle 12 15600    540216.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600     82621.
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284765410.
##  3 cycle 2  15600 241403926.
##  4 cycle 3  15600 283989362.
##  5 cycle 4  15600 260683227.
##  6 cycle 5  15600 200163292.
##  7 cycle 6  15600 134931128.
##  8 cycle 7  15600  55456076.
##  9 cycle 8  15600  19283074.
## 10 cycle 9  15600   9418817.
## 11 cycle 10 15600   3787864.
## 12 cycle 11 15600   1493537.
## 13 cycle 12 15600    591059.
## 14 cycle 13 15600    254219.
## 15 cycle 14 15600    101688.
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284633775.
##  3 cycle 2  15600 242937010.
##  4 cycle 3  15600 283710905.
##  5 cycle 4  15600 259400726.
##  6 cycle 5  15600 201083157.
##  7 cycle 6  15600 135241016.
##  8 cycle 7  15600  55994870.
##  9 cycle 8  15600  20072474.
## 10 cycle 9  15600   9387039.
## 11 cycle 10 15600   3584489.
## 12 cycle 11 15600   1379138.
## 13 cycle 12 15600    559282.
## 14 cycle 13 15600    216086.
## 15 cycle 14 15600    108043.
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 244716203.
##  4 cycle 3  15600 284631283.
##  5 cycle 4  15600 259627517.
##  6 cycle 5  15600 199396470.
##  7 cycle 6  15600 133139994.
##  8 cycle 7  15600  55562043.
##  9 cycle 8  15600  19245299.
## 10 cycle 9  15600   9704813.
## 11 cycle 10 15600   3622622.
## 12 cycle 11 15600   1550736.
## 13 cycle 12 15600    603770.
## 14 cycle 13 15600    190664.
## 15 cycle 14 15600     82621.
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284972266.
##  3 cycle 2  15600 241459867.
##  4 cycle 3  15600 284717094.
##  5 cycle 4  15600 260076816.
##  6 cycle 5  15600 202984585.
##  7 cycle 6  15600 136458725.
##  8 cycle 7  15600  58074164.
##  9 cycle 8  15600  20253094.
## 10 cycle 9  15600   9590414.
## 11 cycle 10 15600   3845063.
## 12 cycle 11 15600   1449049.
## 13 cycle 12 15600    603770.
## 14 cycle 13 15600    184309.
## 15 cycle 14 15600     63555.
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 241119162.
##  4 cycle 3  15600 282901951.
##  5 cycle 4  15600 257811234.
##  6 cycle 5  15600 200897026.
##  7 cycle 6  15600 133737907.
##  8 cycle 7  15600  55882564.
##  9 cycle 8  15600  19264274.
## 10 cycle 9  15600   9209086.
## 11 cycle 10 15600   3654399.
## 12 cycle 11 15600   1353717.
## 13 cycle 12 15600    552926.
## 14 cycle 13 15600    228797.
## 15 cycle 14 15600     82621.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 244270468.
##  4 cycle 3  15600 287176050.
##  5 cycle 4  15600 263169835.
##  6 cycle 5  15600 200801705.
##  7 cycle 6  15600 132810829.
##  8 cycle 7  15600  55869480.
##  9 cycle 8  15600  19421051.
## 10 cycle 9  15600   9615836.
## 11 cycle 10 15600   3895907.
## 12 cycle 11 15600   1576158.
## 13 cycle 12 15600    597415.
## 14 cycle 13 15600    228797.
## 15 cycle 14 15600     76266.
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284784215.
##  3 cycle 2  15600 242620771.
##  4 cycle 3  15600 284999125.
##  5 cycle 4  15600 259894601.
##  6 cycle 5  15600 200162675.
##  7 cycle 6  15600 136055607.
##  8 cycle 7  15600  56824731.
##  9 cycle 8  15600  19945778.
## 10 cycle 9  15600   9704813.
## 11 cycle 10 15600   3749731.
## 12 cycle 11 15600   1455404.
## 13 cycle 12 15600    565637.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600    127110.
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285404783.
##  3 cycle 2  15600 243281792.
##  4 cycle 3  15600 285403077.
##  5 cycle 4  15600 260963311.
##  6 cycle 5  15600 200523495.
##  7 cycle 6  15600 136975888.
##  8 cycle 7  15600  56596805.
##  9 cycle 8  15600  19284655.
## 10 cycle 9  15600   9520504.
## 11 cycle 10 15600   3781509.
## 12 cycle 11 15600   1563447.
## 13 cycle 12 15600    603770.
## 14 cycle 13 15600    247864.
## 15 cycle 14 15600     95332.
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285179122.
##  3 cycle 2  15600 242857420.
##  4 cycle 3  15600 285889913.
##  5 cycle 4  15600 260409096.
##  6 cycle 5  15600 200536823.
##  7 cycle 6  15600 134809248.
##  8 cycle 7  15600  56507352.
##  9 cycle 8  15600  19187616.
## 10 cycle 9  15600   9113754.
## 11 cycle 10 15600   3482801.
## 12 cycle 11 15600   1309228.
## 13 cycle 12 15600    533860.
## 14 cycle 13 15600    139820.
## 15 cycle 14 15600     50844.
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 241892065.
##  4 cycle 3  15600 284833565.
##  5 cycle 4  15600 258121512.
##  6 cycle 5  15600 202638345.
##  7 cycle 6  15600 135492584.
##  8 cycle 7  15600  56895827.
##  9 cycle 8  15600  19505229.
## 10 cycle 9  15600   9755657.
## 11 cycle 10 15600   3724309.
## 12 cycle 11 15600   1264740.
## 13 cycle 12 15600    514794.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600    101688.
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284577359.
##  3 cycle 2  15600 244043278.
##  4 cycle 3  15600 287049505.
##  5 cycle 4  15600 260381902.
##  6 cycle 5  15600 201541836.
##  7 cycle 6  15600 135984759.
##  8 cycle 7  15600  57036088.
##  9 cycle 8  15600  19507109.
## 10 cycle 9  15600   9711168.
## 11 cycle 10 15600   3660755.
## 12 cycle 11 15600   1417271.
## 13 cycle 12 15600    578348.
## 14 cycle 13 15600    171598.
## 15 cycle 14 15600     69910.
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285141512.
##  3 cycle 2  15600 242348893.
##  4 cycle 3  15600 285766312.
##  5 cycle 4  15600 261698122.
##  6 cycle 5  15600 201956689.
##  7 cycle 6  15600 134358741.
##  8 cycle 7  15600  56079510.
##  9 cycle 8  15600  19802846.
## 10 cycle 9  15600   9742946.
## 11 cycle 10 15600   3889552.
## 12 cycle 11 15600   1544381.
## 13 cycle 12 15600    578348.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600    101688.
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 244513315.
##  4 cycle 3  15600 286443272.
##  5 cycle 4  15600 259735484.
##  6 cycle 5  15600 202003673.
##  7 cycle 6  15600 135373828.
##  8 cycle 7  15600  55679306.
##  9 cycle 8  15600  19243805.
## 10 cycle 9  15600   9196375.
## 11 cycle 10 15600   3482801.
## 12 cycle 11 15600   1341006.
## 13 cycle 12 15600    552926.
## 14 cycle 13 15600    146176.
## 15 cycle 14 15600     38133.
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 283449054.
##  3 cycle 2  15600 242752060.
##  4 cycle 3  15600 285125671.
##  5 cycle 4  15600 258391261.
##  6 cycle 5  15600 199938378.
##  7 cycle 6  15600 135107685.
##  8 cycle 7  15600  55806944.
##  9 cycle 8  15600  20109265.
## 10 cycle 9  15600   9412461.
## 11 cycle 10 15600   3546356.
## 12 cycle 11 15600   1461760.
## 13 cycle 12 15600    483016.
## 14 cycle 13 15600    152531.
## 15 cycle 14 15600     50844.
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284821826.
##  3 cycle 2  15600 242785169.
##  4 cycle 3  15600 284179276.
##  5 cycle 4  15600 260436290.
##  6 cycle 5  15600 199909800.
##  7 cycle 6  15600 134149880.
##  8 cycle 7  15600  55545501.
##  9 cycle 8  15600  19319567.
## 10 cycle 9  15600   9431527.
## 11 cycle 10 15600   3857774.
## 12 cycle 11 15600   1474471.
## 13 cycle 12 15600    571993.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600    114399.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284934656.
##  3 cycle 2  15600 244997053.
##  4 cycle 3  15600 287367417.
##  5 cycle 4  15600 262212903.
##  6 cycle 5  15600 202166303.
##  7 cycle 6  15600 134239985.
##  8 cycle 7  15600  55858817.
##  9 cycle 8  15600  19399984.
## 10 cycle 9  15600   9596770.
## 11 cycle 10 15600   3635333.
## 12 cycle 11 15600   1245673.
## 13 cycle 12 15600    502083.
## 14 cycle 13 15600    254219.
## 15 cycle 14 15600     69910.
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284332893.
##  3 cycle 2  15600 243357792.
##  4 cycle 3  15600 286052969.
##  5 cycle 4  15600 260131677.
##  6 cycle 5  15600 198683698.
##  7 cycle 6  15600 134703519.
##  8 cycle 7  15600  56490378.
##  9 cycle 8  15600  20003934.
## 10 cycle 9  15600   9755657.
## 11 cycle 10 15600   3762442.
## 12 cycle 11 15600   1385494.
## 13 cycle 12 15600    559282.
## 14 cycle 13 15600    197020.
## 15 cycle 14 15600     76266.
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285385978.
##  3 cycle 2  15600 242695632.
##  4 cycle 3  15600 286317778.
##  5 cycle 4  15600 260367757.
##  6 cycle 5  15600 199790997.
##  7 cycle 6  15600 135195175.
##  8 cycle 7  15600  55916685.
##  9 cycle 8  15600  19628464.
## 10 cycle 9  15600   9399750.
## 11 cycle 10 15600   3749731.
## 12 cycle 11 15600   1417271.
## 13 cycle 12 15600    546571.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     44488.
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284991071.
##  3 cycle 2  15600 242354927.
##  4 cycle 3  15600 284981501.
##  5 cycle 4  15600 260361232.
##  6 cycle 5  15600 200308135.
##  7 cycle 6  15600 135557163.
##  8 cycle 7  15600  56220376.
##  9 cycle 8  15600  19762593.
## 10 cycle 9  15600   9501438.
## 11 cycle 10 15600   3660755.
## 12 cycle 11 15600   1455404.
## 13 cycle 12 15600    565637.
## 14 cycle 13 15600    190664.
## 15 cycle 14 15600     69910.
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284671385.
##  3 cycle 2  15600 245471820.
##  4 cycle 3  15600 285395526.
##  5 cycle 4  15600 260183637.
##  6 cycle 5  15600 198655121.
##  7 cycle 6  15600 133809784.
##  8 cycle 7  15600  56088530.
##  9 cycle 8  15600  19552702.
## 10 cycle 9  15600   9450594.
## 11 cycle 10 15600   3724309.
## 12 cycle 11 15600   1461760.
## 13 cycle 12 15600    521149.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600     69910.
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284445724.
##  3 cycle 2  15600 242516064.
##  4 cycle 3  15600 284020845.
##  5 cycle 4  15600 259703384.
##  6 cycle 5  15600 200625111.
##  7 cycle 6  15600 134524876.
##  8 cycle 7  15600  56440897.
##  9 cycle 8  15600  19780584.
## 10 cycle 9  15600   9742946.
## 11 cycle 10 15600   3705243.
## 12 cycle 11 15600   1347361.
## 13 cycle 12 15600    540216.
## 14 cycle 13 15600    216086.
## 15 cycle 14 15600     63555.
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 242715855.
##  4 cycle 3  15600 283172437.
##  5 cycle 4  15600 261905626.
##  6 cycle 5  15600 201125096.
##  7 cycle 6  15600 134677484.
##  8 cycle 7  15600  56082363.
##  9 cycle 8  15600  20316679.
## 10 cycle 9  15600   9736590.
## 11 cycle 10 15600   3737020.
## 12 cycle 11 15600   1366428.
## 13 cycle 12 15600    419461.
## 14 cycle 13 15600    177953.
## 15 cycle 14 15600     63555.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285028682.
##  3 cycle 2  15600 243266298.
##  4 cycle 3  15600 285213584.
##  5 cycle 4  15600 258962099.
##  6 cycle 5  15600 199779572.
##  7 cycle 6  15600 134388420.
##  8 cycle 7  15600  56170001.
##  9 cycle 8  15600  19648161.
## 10 cycle 9  15600   9641258.
## 11 cycle 10 15600   3787864.
## 12 cycle 11 15600   1487182.
## 13 cycle 12 15600    597415.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     95332.
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 242989690.
##  4 cycle 3  15600 285436433.
##  5 cycle 4  15600 264645459.
##  6 cycle 5  15600 203740533.
##  7 cycle 6  15600 136456110.
##  8 cycle 7  15600  56426920.
##  9 cycle 8  15600  19555566.
## 10 cycle 9  15600   9418817.
## 11 cycle 10 15600   3768798.
## 12 cycle 11 15600   1436338.
## 13 cycle 12 15600    591059.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     69910.
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284840631.
##  3 cycle 2  15600 244906535.
##  4 cycle 3  15600 285899777.
##  5 cycle 4  15600 261591773.
##  6 cycle 5  15600 201846754.
##  7 cycle 6  15600 133675414.
##  8 cycle 7  15600  55447200.
##  9 cycle 8  15600  19715119.
## 10 cycle 9  15600   9514149.
## 11 cycle 10 15600   3857774.
## 12 cycle 11 15600   1480826.
## 13 cycle 12 15600    533860.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600     63555.
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284915851.
##  3 cycle 2  15600 241432140.
##  4 cycle 3  15600 284120304.
##  5 cycle 4  15600 261971158.
##  6 cycle 5  15600 203346040.
##  7 cycle 6  15600 136256113.
##  8 cycle 7  15600  57287300.
##  9 cycle 8  15600  20244590.
## 10 cycle 9  15600   9666680.
## 11 cycle 10 15600   3628977.
## 12 cycle 11 15600   1347361.
## 13 cycle 12 15600    559282.
## 14 cycle 13 15600    247864.
## 15 cycle 14 15600     88977.
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 242671983.
##  4 cycle 3  15600 283584360.
##  5 cycle 4  15600 261460187.
##  6 cycle 5  15600 201458626.
##  7 cycle 6  15600 135005091.
##  8 cycle 7  15600  57342026.
##  9 cycle 8  15600  19939839.
## 10 cycle 9  15600   9545926.
## 11 cycle 10 15600   3717954.
## 12 cycle 11 15600   1398205.
## 13 cycle 12 15600    546571.
## 14 cycle 13 15600    254219.
## 15 cycle 14 15600    101688.
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285291953.
##  3 cycle 2  15600 242266530.
##  4 cycle 3  15600 285225970.
##  5 cycle 4  15600 259155694.
##  6 cycle 5  15600 201017082.
##  7 cycle 6  15600 134817054.
##  8 cycle 7  15600  55834754.
##  9 cycle 8  15600  19228678.
## 10 cycle 9  15600   9463305.
## 11 cycle 10 15600   3641688.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    591059.
## 14 cycle 13 15600    209731.
## 15 cycle 14 15600    139820.
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284464529.
##  3 cycle 2  15600 243413081.
##  4 cycle 3  15600 286089480.
##  5 cycle 4  15600 261352456.
##  6 cycle 5  15600 200972621.
##  7 cycle 6  15600 134207181.
##  8 cycle 7  15600  56213459.
##  9 cycle 8  15600  19958041.
## 10 cycle 9  15600   9514149.
## 11 cycle 10 15600   3895907.
## 12 cycle 11 15600   1525314.
## 13 cycle 12 15600    552926.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600     82621.
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284389309.
##  3 cycle 2  15600 241465249.
##  4 cycle 3  15600 282996382.
##  5 cycle 4  15600 258394262.
##  6 cycle 5  15600 200982792.
##  7 cycle 6  15600 136400904.
##  8 cycle 7  15600  56972802.
##  9 cycle 8  15600  20071192.
## 10 cycle 9  15600   9863700.
## 11 cycle 10 15600   3813286.
## 12 cycle 11 15600   1474471.
## 13 cycle 12 15600    603770.
## 14 cycle 13 15600    222442.
## 15 cycle 14 15600    108043.
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285047487.
##  3 cycle 2  15600 244308306.
##  4 cycle 3  15600 287490598.
##  5 cycle 4  15600 259460543.
##  6 cycle 5  15600 199035015.
##  7 cycle 6  15600 134744668.
##  8 cycle 7  15600  56179482.
##  9 cycle 8  15600  19437373.
## 10 cycle 9  15600   9526860.
## 11 cycle 10 15600   3711598.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    495727.
## 14 cycle 13 15600    203375.
## 15 cycle 14 15600     69910.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285009876.
##  3 cycle 2  15600 243868605.
##  4 cycle 3  15600 285016960.
##  5 cycle 4  15600 260865965.
##  6 cycle 5  15600 200533650.
##  7 cycle 6  15600 136654030.
##  8 cycle 7  15600  57344130.
##  9 cycle 8  15600  19641026.
## 10 cycle 9  15600   9603125.
## 11 cycle 10 15600   3628977.
## 12 cycle 11 15600   1391849.
## 13 cycle 12 15600    495727.
## 14 cycle 13 15600    216086.
## 15 cycle 14 15600     82621.
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 284708995.
##  3 cycle 2  15600 244786498.
##  4 cycle 3  15600 287687221.
##  5 cycle 4  15600 263820063.
##  6 cycle 5  15600 201396977.
##  7 cycle 6  15600 134621750.
##  8 cycle 7  15600  55964784.
##  9 cycle 8  15600  19599579.
## 10 cycle 9  15600   9266285.
## 11 cycle 10 15600   3520934.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    565637.
## 14 cycle 13 15600    266930.
## 15 cycle 14 15600    120754.
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  15600 440865997.
##  2 cycle 1  15600 285348368.
##  3 cycle 2  15600 243616950.
##  4 cycle 3  15600 286674303.
##  5 cycle 4  15600 261510191.
##  6 cycle 5  15600 201937615.
##  7 cycle 6  15600 135831642.
##  8 cycle 7  15600  55975330.
##  9 cycle 8  15600  19407891.
## 10 cycle 9  15600   9253574.
## 11 cycle 10 15600   3667110.
## 12 cycle 11 15600   1429982.
## 13 cycle 12 15600    571993.
## 14 cycle 13 15600    254219.
## 15 cycle 14 15600    114399.
m.M <- m.C <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M[, 1] <- v.M_1_females

The same reasoning is applied to female patients:

#Females
Probs <- function(state){
  return(transition_prob_f_alt[[state]])
}
Costs <- function(state) {
  return(transition_costs_f[[state]])
}
# Testing 
set.seed(1) #deterministic sequence of random numbers

transition_prob_f_altB <- transition_prob_f_altB %>% 
  map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altB <- function(n.t) {
     for (t in 1:n.t) {
      m.p <- m.M_altB[, t]
# calculate the transition probabilities at cycle t
     #state <- list("P", "MPD", "APD","D")
      for (i in 1:length(m.p)) {
        current_state <- m.p[i]
        new_state <- m.p[i]
         if (t > 10) {
           new_state <- sample(names(transition_prob_f_altB[[10]][[current_state]]), 1, prob = transition_prob_f_altB[[10]][[current_state]])
         } else {
           new_state <- sample(names(transition_prob_f_altB[[t]][[current_state]]), 1, prob = transition_prob_f_altB[[t]][[current_state]])
         }
        m.M_altB[i, t + 1] <- new_state
        #m.C[i, t + 1] <- Costs(current_state)
      }   
    } # close the loop for the time points
  return(m.M_altB)
}

# Init m.M #repeat it!!!!
model_results_f_altB <- list()
for(i in 1:n.sim) {
m.M_altB <- m.C_altB <-  matrix(nrow = n_females,
                      ncol = n.t + 1,
                      dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " "))) 
m.M_altB[, 1] <- v.M_1_females
# Microsim loop
model_results_f_altB[[i]] <- loop_microsim_altB(n.t)
print(i)
}  
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
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## [1] 93
## [1] 94
## [1] 95
## [1] 96
## [1] 97
## [1] 98
## [1] 99
## [1] 100
# repeat it!!!

#Results of the median simulation, the 50th
model_results_f_altB[[50]][1:300, ]
##         cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 2   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 3   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 4   "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 5   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 6   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 7   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 8   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 9   "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 10  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 11  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 12  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 13  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 14  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 15  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 16  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 17  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 18  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 19  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 20  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"    
## ind 21  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 22  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"    
## ind 23  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 24  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 25  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 26  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 27  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 28  "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 29  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 30  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 31  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 32  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 33  "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 34  "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 35  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 36  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 37  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 38  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 39  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 40  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 41  "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 42  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 43  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 44  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 45  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 46  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 47  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 48  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 49  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 50  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 51  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 52  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 53  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 54  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 55  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 56  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 57  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 58  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 59  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 60  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 61  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 62  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 63  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 64  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 65  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 66  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 67  "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 68  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 69  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 70  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 71  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 72  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 73  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 74  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 75  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 76  "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 77  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 78  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 79  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 80  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 81  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 82  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 83  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 84  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 85  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 86  "P"     "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"    
## ind 87  "P"     "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 88  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 89  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 90  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 91  "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 92  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 93  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 94  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 95  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"    
## ind 96  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 97  "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 98  "P"     "D"     "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 99  "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 100 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 101 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 102 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 103 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 104 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 105 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 106 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"  
## ind 107 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 108 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 109 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 110 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 111 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 112 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 113 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 114 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 115 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 116 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 117 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 118 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 119 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 120 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 121 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 122 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 123 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 124 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 125 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 126 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 127 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 128 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"  
## ind 129 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 130 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 131 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 132 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 133 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 134 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 135 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 136 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 137 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 138 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 139 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 140 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 141 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 142 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 143 "P"     "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 144 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 145 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 146 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 147 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 148 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"  
## ind 149 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 150 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 151 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 152 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 153 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 154 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 155 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 156 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 157 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 158 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 159 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 160 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 161 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 162 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 163 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 164 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 165 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 166 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 167 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 168 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 169 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 170 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 171 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 172 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 173 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 174 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 175 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 176 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 177 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 178 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 179 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 180 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 181 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 182 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 183 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 184 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 185 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 186 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 187 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 188 "P"     "MPD"   "D"     "D"     "D"     "D"     "D"     "D"     "D"    
## ind 189 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 190 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 191 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 192 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"     "D"     "D"    
## ind 193 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 194 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 195 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 196 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 197 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 198 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 199 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 200 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 201 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 202 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "D"    
## ind 203 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 204 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 205 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 206 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 207 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 208 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 209 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 210 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 211 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 212 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 213 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 214 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 215 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 216 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 217 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 218 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 219 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 220 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 221 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 222 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 223 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 224 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "D"    
## ind 225 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 226 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 227 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 228 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 229 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 230 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 231 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 232 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 233 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 234 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 235 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 236 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 237 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 238 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 239 "P"     "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 240 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 241 "P"     "MPD"   "MPD"   "APD"   "D"     "D"     "D"     "D"     "D"    
## ind 242 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 243 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 244 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 245 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 246 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 247 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 248 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 249 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 250 "P"     "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"     "D"    
## ind 251 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 252 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "APD"  
## ind 253 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 254 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 255 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 256 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 257 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 258 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 259 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 260 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 261 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 262 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 263 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 264 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 265 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 266 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 267 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 268 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 269 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 270 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 271 "P"     "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"   "APD"   "D"    
## ind 272 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 273 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 274 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 275 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 276 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 277 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 278 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 279 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 280 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "APD"   "APD"  
## ind 281 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 282 "P"     "MPD"   "APD"   "APD"   "APD"   "D"     "D"     "D"     "D"    
## ind 283 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"    
## ind 284 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 285 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 286 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 287 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 288 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 289 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 290 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 291 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 292 "P"     "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"    
## ind 293 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"    
## ind 294 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 295 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "MPD"  
## ind 296 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
## ind 297 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"    
## ind 298 "P"     "MPD"   "MPD"   "D"     "D"     "D"     "D"     "D"     "D"    
## ind 299 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "MPD"   "APD"   "D"     "D"    
## ind 300 "P"     "MPD"   "MPD"   "MPD"   "MPD"   "D"     "D"     "D"     "D"    
##         cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 2   "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 3   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 4   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 5   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 6   "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 7   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 8   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 9   "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 10  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 11  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 12  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 13  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 14  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 15  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 16  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 17  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 18  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 19  "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 20  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 21  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 22  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 23  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 24  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 25  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 26  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 27  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 28  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 29  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 30  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 31  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 32  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 33  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 34  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 35  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 36  "MPD"   "MPD"    "MPD"    "MPD"    "D"      "D"      "D"     
## ind 37  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 38  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 39  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 40  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 41  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 42  "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "MPD"    "MPD"   
## ind 43  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 44  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 45  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 46  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 47  "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 48  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 49  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 50  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 51  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 52  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 53  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 54  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 55  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 56  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 57  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 58  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 59  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 60  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 61  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 62  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 63  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 64  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 65  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 66  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 67  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 68  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 69  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 70  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 71  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 72  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 73  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 74  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 75  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 76  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 77  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 78  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 79  "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 80  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 81  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 82  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 83  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 84  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 85  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 86  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 87  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 88  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 89  "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 90  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 91  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 92  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 93  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 94  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 95  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 96  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 97  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 98  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 99  "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 100 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 101 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 102 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 103 "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 104 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 105 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 106 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 107 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 108 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 109 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 110 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 111 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 112 "MPD"   "MPD"    "MPD"    "MPD"    "D"      "D"      "D"     
## ind 113 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 114 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 115 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 116 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 117 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 118 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 119 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 120 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 121 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 122 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 123 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 124 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 125 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 126 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 127 "MPD"   "MPD"    "MPD"    "MPD"    "D"      "D"      "D"     
## ind 128 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 129 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 130 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 131 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 132 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 133 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 134 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 135 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 136 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 137 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 138 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 139 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 140 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 141 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 142 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 143 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 144 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 145 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 146 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 147 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 148 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 149 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 150 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 151 "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 152 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 153 "APD"   "APD"    "D"      "D"      "D"      "D"      "D"     
## ind 154 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 155 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 156 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 157 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 158 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 159 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 160 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 161 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 162 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 163 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 164 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 165 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 166 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 167 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 168 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 169 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 170 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 171 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 172 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 173 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 174 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 175 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 176 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 177 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 178 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 179 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 180 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 181 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 182 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 183 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 184 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 185 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 186 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "MPD"    "D"     
## ind 187 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 188 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 189 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 190 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 191 "MPD"   "MPD"    "MPD"    "MPD"    "D"      "D"      "D"     
## ind 192 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 193 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 194 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 195 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 196 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 197 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 198 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 199 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 200 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 201 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 202 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 203 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 204 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 205 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 206 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 207 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 208 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 209 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 210 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 211 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 212 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 213 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 214 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 215 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 216 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 217 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 218 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 219 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 220 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 221 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 222 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 223 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 224 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 225 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 226 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 227 "MPD"   "MPD"    "MPD"    "MPD"    "MPD"    "D"      "D"     
## ind 228 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 229 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 230 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 231 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 232 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 233 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 234 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 235 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 236 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 237 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 238 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 239 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 240 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 241 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 242 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 243 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 244 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 245 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 246 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 247 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 248 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 249 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 250 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 251 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 252 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 253 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 254 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 255 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 256 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 257 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 258 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 259 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 260 "MPD"   "D"      "D"      "D"      "D"      "D"      "D"     
## ind 261 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 262 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 263 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 264 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 265 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 266 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 267 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 268 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 269 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 270 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 271 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 272 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 273 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 274 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 275 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 276 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 277 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 278 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 279 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 280 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 281 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 282 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 283 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 284 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 285 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 286 "MPD"   "MPD"    "MPD"    "D"      "D"      "D"      "D"     
## ind 287 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 288 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 289 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 290 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 291 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 292 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 293 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 294 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 295 "MPD"   "MPD"    "D"      "D"      "D"      "D"      "D"     
## ind 296 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 297 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 298 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 299 "D"     "D"      "D"      "D"      "D"      "D"      "D"     
## ind 300 "D"     "D"      "D"      "D"      "D"      "D"      "D"
df_m.M_altB <- model_results_f_altB[[50]] %>% as.tibble()
library(janitor)
map(
  c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
    "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
  ~ df_m.M_altB %>% tabyl(!!sym(.x))
)
## [[1]]
##  cycle 0     n percent
##        P 10400       1
## 
## [[2]]
##  cycle 1     n percent
##        D   143 0.01375
##      MPD 10257 0.98625
## 
## [[3]]
##  cycle 2    n    percent
##      APD  201 0.01932692
##        D  407 0.03913462
##      MPD 9792 0.94153846
## 
## [[4]]
##  cycle 3    n    percent
##      APD  459 0.04413462
##        D  672 0.06461538
##      MPD 9269 0.89125000
## 
## [[5]]
##  cycle 4    n    percent
##      APD  591 0.05682692
##        D 1291 0.12413462
##      MPD 8518 0.81903846
## 
## [[6]]
##  cycle 5    n    percent
##      APD  688 0.06615385
##        D 2127 0.20451923
##      MPD 7585 0.72932692
## 
## [[7]]
##  cycle 6    n    percent
##      APD  643 0.06182692
##        D 3335 0.32067308
##      MPD 6422 0.61750000
## 
## [[8]]
##  cycle 7    n    percent
##      APD  461 0.04432692
##        D 4906 0.47173077
##      MPD 5033 0.48394231
## 
## [[9]]
##  cycle 8    n    percent
##      APD  226 0.02173077
##        D 6675 0.64182692
##      MPD 3499 0.33644231
## 
## [[10]]
##  cycle 9    n     percent
##      APD   68 0.006538462
##        D 8303 0.798365385
##      MPD 2029 0.195096154
## 
## [[11]]
##  cycle 10    n     percent
##       APD   21 0.002019231
##         D 9234 0.887884615
##       MPD 1145 0.110096154
## 
## [[12]]
##  cycle 11    n      percent
##       APD    6 0.0005769231
##         D 9745 0.9370192308
##       MPD  649 0.0624038462
## 
## [[13]]
##  cycle 12     n      percent
##       APD     5 0.0004807692
##         D 10052 0.9665384615
##       MPD   343 0.0329807692
## 
## [[14]]
##  cycle 13     n       percent
##       APD     1 0.00009615385
##         D 10214 0.98211538462
##       MPD   185 0.01778846154
## 
## [[15]]
##  cycle 14     n      percent
##       APD     2 0.0001923077
##         D 10305 0.9908653846
##       MPD    93 0.0089423077
#Transition costs
transition_costs_f_alt <-
  transition_costs_f_alt %>% 
  data.table::rbindlist() %>% 
  t() %>% 
  as_tibble(rownames = "Stage") %>% 
  rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
    "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>% 
  pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")

final_cost_f_altB <- map(
    model_results_f_altB,
    ~ .x %>% 
      as_tibble() %>% 
      mutate(id = row_number()) %>% 
      pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>% 
      left_join(
        transition_costs_f_alt
      )
  )
 

final_cost_f2_altB <-
  map(
    final_cost_f_altB,
    ~ .x %>% 
      group_by(cycle) %>% 
      summarise(
      n = n(),
      sum_costs = sum(cost, na.rm = TRUE)
    ) %>% 
    mutate(cycle = as_factor (cycle) %>%  fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%  arrange(cycle) %>% 
    filter(cycle != "cycle 15")
  )
final_cost_f2_altB
## [[1]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 176586793.
##  4 cycle 3  10400 166351977.
##  5 cycle 4  10400 208804573.
##  6 cycle 5  10400 172728556.
##  7 cycle 6  10400 155268280.
##  8 cycle 7  10400  80046358.
##  9 cycle 8  10400  17505420.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 176168078.
##  4 cycle 3  10400 166270008.
##  5 cycle 4  10400 206144721.
##  6 cycle 5  10400 168454297.
##  7 cycle 6  10400 151529220.
##  8 cycle 7  10400  79195196.
##  9 cycle 8  10400  18299480.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400         0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 176986079.
##  4 cycle 3  10400 166115296.
##  5 cycle 4  10400 206245168.
##  6 cycle 5  10400 171412710.
##  7 cycle 6  10400 152284197.
##  8 cycle 7  10400  79115652.
##  9 cycle 8  10400  17671989.
## 10 cycle 9  10400    701611.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     11694.
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 174379076.
##  4 cycle 3  10400 164926891.
##  5 cycle 4  10400 206252486.
##  6 cycle 5  10400 168886163.
##  7 cycle 6  10400 153024612.
##  8 cycle 7  10400  78524668.
##  9 cycle 8  10400  17202384.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     11694.
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 175711517.
##  4 cycle 3  10400 164920565.
##  5 cycle 4  10400 205450012.
##  6 cycle 5  10400 169787826.
##  7 cycle 6  10400 151303765.
##  8 cycle 7  10400  78965489.
##  9 cycle 8  10400  17816502.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 175484856.
##  4 cycle 3  10400 164776925.
##  5 cycle 4  10400 206990757.
##  6 cycle 5  10400 170656732.
##  7 cycle 6  10400 152714039.
##  8 cycle 7  10400  79670955.
##  9 cycle 8  10400  17204828.
## 10 cycle 9  10400    689918.
## 11 cycle 10 10400    152016.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     23387.
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 176120721.
##  4 cycle 3  10400 166776048.
##  5 cycle 4  10400 206051902.
##  6 cycle 5  10400 169465012.
##  7 cycle 6  10400 150240405.
##  8 cycle 7  10400  78641378.
##  9 cycle 8  10400  17813880.
## 10 cycle 9  10400    970562.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     23387.
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249629414.
##  3 cycle 2  10400 176438450.
##  4 cycle 3  10400 164675188.
##  5 cycle 4  10400 205127199.
##  6 cycle 5  10400 169077532.
##  7 cycle 6  10400 151295710.
##  8 cycle 7  10400  78488991.
##  9 cycle 8  10400  17458427.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 175754218.
##  4 cycle 3  10400 164527857.
##  5 cycle 4  10400 204958648.
##  6 cycle 5  10400 169185719.
##  7 cycle 6  10400 150696214.
##  8 cycle 7  10400  77509221.
##  9 cycle 8  10400  16699617.
## 10 cycle 9  10400    724998.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     11694.
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250506674.
##  3 cycle 2  10400 176983246.
##  4 cycle 3  10400 167221997.
##  5 cycle 4  10400 206692487.
##  6 cycle 5  10400 170704995.
##  7 cycle 6  10400 153655166.
##  8 cycle 7  10400  80751074.
##  9 cycle 8  10400  17479211.
## 10 cycle 9  10400    678224.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     81855.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     23387.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 176620993.
##  4 cycle 3  10400 167432055.
##  5 cycle 4  10400 205909998.
##  6 cycle 5  10400 168222001.
##  7 cycle 6  10400 150486094.
##  8 cycle 7  10400  77951530.
##  9 cycle 8  10400  16885976.
## 10 cycle 9  10400    724998.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400     35081.
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 175479189.
##  4 cycle 3  10400 165712572.
##  5 cycle 4  10400 206877952.
##  6 cycle 5  10400 170068417.
##  7 cycle 6  10400 152015893.
##  8 cycle 7  10400  79421177.
##  9 cycle 8  10400  17189191.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 176366810.
##  4 cycle 3  10400 165657751.
##  5 cycle 4  10400 206459076.
##  6 cycle 5  10400 168545681.
##  7 cycle 6  10400 150147040.
##  8 cycle 7  10400  77999850.
##  9 cycle 8  10400  17893955.
## 10 cycle 9  10400    818546.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     81855.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249434467.
##  3 cycle 2  10400 175075653.
##  4 cycle 3  10400 165445583.
##  5 cycle 4  10400 206521001.
##  6 cycle 5  10400 168957716.
##  7 cycle 6  10400 153231382.
##  8 cycle 7  10400  78720919.
##  9 cycle 8  10400  18381721.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250019307.
##  3 cycle 2  10400 176296180.
##  4 cycle 3  10400 166797661.
##  5 cycle 4  10400 207927161.
##  6 cycle 5  10400 170589488.
##  7 cycle 6  10400 151598809.
##  8 cycle 7  10400  79026444.
##  9 cycle 8  10400  17849666.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     23387.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     70161.
## 15 cycle 14 10400     35081.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 176347382.
##  4 cycle 3  10400 166101854.
##  5 cycle 4  10400 205190747.
##  6 cycle 5  10400 168930565.
##  7 cycle 6  10400 150763033.
##  8 cycle 7  10400  79980942.
##  9 cycle 8  10400  18256005.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 176443711.
##  4 cycle 3  10400 165709671.
##  5 cycle 4  10400 204826651.
##  6 cycle 5  10400 168702130.
##  7 cycle 6  10400 152192379.
##  8 cycle 7  10400  79714075.
##  9 cycle 8  10400  18857744.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     58468.
## 15 cycle 14 10400     11694.
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 175351087.
##  4 cycle 3  10400 164929791.
##  5 cycle 4  10400 206348066.
##  6 cycle 5  10400 169290013.
##  7 cycle 6  10400 151076568.
##  8 cycle 7  10400  79330484.
##  9 cycle 8  10400  17599683.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     23387.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249726887.
##  3 cycle 2  10400 176027423.
##  4 cycle 3  10400 163848917.
##  5 cycle 4  10400 204666384.
##  6 cycle 5  10400 167876329.
##  7 cycle 6  10400 151650486.
##  8 cycle 7  10400  78824249.
##  9 cycle 8  10400  17498823.
## 10 cycle 9  10400    713305.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     11694.
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 176342121.
##  4 cycle 3  10400 165202316.
##  5 cycle 4  10400 205514388.
##  6 cycle 5  10400 168524552.
##  7 cycle 6  10400 151691981.
##  8 cycle 7  10400  78653271.
##  9 cycle 8  10400  17004818.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249751255.
##  3 cycle 2  10400 174412265.
##  4 cycle 3  10400 165068161.
##  5 cycle 4  10400 206734426.
##  6 cycle 5  10400 169280098.
##  7 cycle 6  10400 154169158.
##  8 cycle 7  10400  80677480.
##  9 cycle 8  10400  18448782.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 175564997.
##  4 cycle 3  10400 166939456.
##  5 cycle 4  10400 206878918.
##  6 cycle 5  10400 169152529.
##  7 cycle 6  10400 150976696.
##  8 cycle 7  10400  79703659.
##  9 cycle 8  10400  17728201.
## 10 cycle 9  10400    572982.
## 11 cycle 10 10400    163709.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     11694.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 175356348.
##  4 cycle 3  10400 165421335.
##  5 cycle 4  10400 205662780.
##  6 cycle 5  10400 167437126.
##  7 cycle 6  10400 149978801.
##  8 cycle 7  10400  79219724.
##  9 cycle 8  10400  17867747.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 175557913.
##  4 cycle 3  10400 165466930.
##  5 cycle 4  10400 205801094.
##  6 cycle 5  10400 168769375.
##  7 cycle 6  10400 151460792.
##  8 cycle 7  10400  78419111.
##  9 cycle 8  10400  17371934.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 174789696.
##  4 cycle 3  10400 164932427.
##  5 cycle 4  10400 205214979.
##  6 cycle 5  10400 167079392.
##  7 cycle 6  10400 150228420.
##  8 cycle 7  10400  78363356.
##  9 cycle 8  10400  16708022.
## 10 cycle 9  10400    619756.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    175403.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 175624699.
##  4 cycle 3  10400 164913715.
##  5 cycle 4  10400 206763041.
##  6 cycle 5  10400 169642573.
##  7 cycle 6  10400 153853107.
##  8 cycle 7  10400  80857376.
##  9 cycle 8  10400  18539429.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400     23387.
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 174775935.
##  4 cycle 3  10400 164463020.
##  5 cycle 4  10400 205375729.
##  6 cycle 5  10400 167453513.
##  7 cycle 6  10400 151351318.
##  8 cycle 7  10400  78352951.
##  9 cycle 8  10400  17782068.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 176370654.
##  4 cycle 3  10400 166110815.
##  5 cycle 4  10400 206695731.
##  6 cycle 5  10400 170127892.
##  7 cycle 6  10400 151645975.
##  8 cycle 7  10400  78863649.
##  9 cycle 8  10400  16709015.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     23387.
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 174466300.
##  4 cycle 3  10400 165175958.
##  5 cycle 4  10400 206251347.
##  6 cycle 5  10400 170122718.
##  7 cycle 6  10400 153402777.
##  8 cycle 7  10400  78682266.
##  9 cycle 8  10400  17399771.
## 10 cycle 9  10400    689918.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250287359.
##  3 cycle 2  10400 176413761.
##  4 cycle 3  10400 165418700.
##  5 cycle 4  10400 205436032.
##  6 cycle 5  10400 168088377.
##  7 cycle 6  10400 150337314.
##  8 cycle 7  10400  78083101.
##  9 cycle 8  10400  17708947.
## 10 cycle 9  10400    701611.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     93548.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249507572.
##  3 cycle 2  10400 174980334.
##  4 cycle 3  10400 164383951.
##  5 cycle 4  10400 204800003.
##  6 cycle 5  10400 168000021.
##  7 cycle 6  10400 151762343.
##  8 cycle 7  10400  78905273.
##  9 cycle 8  10400  17331261.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     35081.
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 176060009.
##  4 cycle 3  10400 166148504.
##  5 cycle 4  10400 204559758.
##  6 cycle 5  10400 168440937.
##  7 cycle 6  10400 151790050.
##  8 cycle 7  10400  79507412.
##  9 cycle 8  10400  17138761.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400     23387.
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250336096.
##  3 cycle 2  10400 175977034.
##  4 cycle 3  10400 165923160.
##  5 cycle 4  10400 207935134.
##  6 cycle 5  10400 171251503.
##  7 cycle 6  10400 153791380.
##  8 cycle 7  10400  80878938.
##  9 cycle 8  10400  18901120.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249873097.
##  3 cycle 2  10400 175067558.
##  4 cycle 3  10400 164072947.
##  5 cycle 4  10400 205892946.
##  6 cycle 5  10400 170023149.
##  7 cycle 6  10400 153448783.
##  8 cycle 7  10400  79653110.
##  9 cycle 8  10400  17573474.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 176840570.
##  4 cycle 3  10400 166712001.
##  5 cycle 4  10400 206584066.
##  6 cycle 5  10400 169928354.
##  7 cycle 6  10400 150939326.
##  8 cycle 7  10400  77877938.
##  9 cycle 8  10400  17254085.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    175403.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 175543141.
##  4 cycle 3  10400 165617427.
##  5 cycle 4  10400 205451806.
##  6 cycle 5  10400 167724604.
##  7 cycle 6  10400 151026632.
##  8 cycle 7  10400  78046679.
##  9 cycle 8  10400  17388744.
## 10 cycle 9  10400    689918.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 176123554.
##  4 cycle 3  10400 165863593.
##  5 cycle 4  10400 206790345.
##  6 cycle 5  10400 170413640.
##  7 cycle 6  10400 153830105.
##  8 cycle 7  10400  80345192.
##  9 cycle 8  10400  16957369.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400     23387.
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 176273918.
##  4 cycle 3  10400 164592429.
##  5 cycle 4  10400 205028374.
##  6 cycle 5  10400 168536198.
##  7 cycle 6  10400 151042935.
##  8 cycle 7  10400  78104663.
##  9 cycle 8  10400  17455169.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     70161.
## 15 cycle 14 10400     11694.
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 176232228.
##  4 cycle 3  10400 165070531.
##  5 cycle 4  10400 204603976.
##  6 cycle 5  10400 168250433.
##  7 cycle 6  10400 150669087.
##  8 cycle 7  10400  79554987.
##  9 cycle 8  10400  17388565.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400         0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 177034041.
##  4 cycle 3  10400 165601875.
##  5 cycle 4  10400 205776068.
##  6 cycle 5  10400 170008941.
##  7 cycle 6  10400 152176657.
##  8 cycle 7  10400  78636922.
##  9 cycle 8  10400  16967403.
## 10 cycle 9  10400    666531.
## 11 cycle 10 10400    128629.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     35081.
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 175640887.
##  4 cycle 3  10400 164392122.
##  5 cycle 4  10400 206892415.
##  6 cycle 5  10400 169243897.
##  7 cycle 6  10400 153340082.
##  8 cycle 7  10400  78161903.
##  9 cycle 8  10400  17548895.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     23387.
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 175895477.
##  4 cycle 3  10400 164575562.
##  5 cycle 4  10400 206418448.
##  6 cycle 5  10400 170879994.
##  7 cycle 6  10400 154419551.
##  8 cycle 7  10400  80460416.
##  9 cycle 8  10400  18214973.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249361362.
##  3 cycle 2  10400 174683450.
##  4 cycle 3  10400 164574773.
##  5 cycle 4  10400 205038454.
##  6 cycle 5  10400 168444814.
##  7 cycle 6  10400 150481389.
##  8 cycle 7  10400  77835560.
##  9 cycle 8  10400  17380339.
## 10 cycle 9  10400    654837.
## 11 cycle 10 10400    152016.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250555411.
##  3 cycle 2  10400 176780264.
##  4 cycle 3  10400 165476156.
##  5 cycle 4  10400 205324849.
##  6 cycle 5  10400 168033211.
##  7 cycle 6  10400 150688159.
##  8 cycle 7  10400  77916589.
##  9 cycle 8  10400  16936406.
## 10 cycle 9  10400    783466.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     58468.
## 15 cycle 14 10400     23387.
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 176300431.
##  4 cycle 3  10400 165445848.
##  5 cycle 4  10400 206113516.
##  6 cycle 5  10400 169475776.
##  7 cycle 6  10400 153183055.
##  8 cycle 7  10400  80029256.
##  9 cycle 8  10400  17735970.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 176739990.
##  4 cycle 3  10400 166681693.
##  5 cycle 4  10400 205255606.
##  6 cycle 5  10400 168587488.
##  7 cycle 6  10400 151120965.
##  8 cycle 7  10400  77851173.
##  9 cycle 8  10400  17513825.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     35081.
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249678150.
##  3 cycle 2  10400 176122138.
##  4 cycle 3  10400 166055994.
##  5 cycle 4  10400 206982955.
##  6 cycle 5  10400 170115814.
##  7 cycle 6  10400 153752655.
##  8 cycle 7  10400  80267880.
##  9 cycle 8  10400  17614506.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    280644.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 176170505.
##  4 cycle 3  10400 165214702.
##  5 cycle 4  10400 206119522.
##  6 cycle 5  10400 169463714.
##  7 cycle 6  10400 151587791.
##  8 cycle 7  10400  79339406.
##  9 cycle 8  10400  17735791.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    444354.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     81855.
## 14 cycle 13 10400     70161.
## 15 cycle 14 10400         0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 176641432.
##  4 cycle 3  10400 165509364.
##  5 cycle 4  10400 205462196.
##  6 cycle 5  10400 169579654.
##  7 cycle 6  10400 151879485.
##  8 cycle 7  10400  80020339.
##  9 cycle 8  10400  17112552.
## 10 cycle 9  10400    654837.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     23387.
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 175554069.
##  4 cycle 3  10400 164798537.
##  5 cycle 4  10400 205990632.
##  6 cycle 5  10400 169336562.
##  7 cycle 6  10400 152518480.
##  8 cycle 7  10400  79710355.
##  9 cycle 8  10400  17809727.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     23387.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 177028780.
##  4 cycle 3  10400 165412639.
##  5 cycle 4  10400 205545592.
##  6 cycle 5  10400 167872869.
##  7 cycle 6  10400 152240381.
##  8 cycle 7  10400  80383845.
##  9 cycle 8  10400  17748806.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250238623.
##  3 cycle 2  10400 176210373.
##  4 cycle 3  10400 164293287.
##  5 cycle 4  10400 206168296.
##  6 cycle 5  10400 169737817.
##  7 cycle 6  10400 152992394.
##  8 cycle 7  10400  80073115.
##  9 cycle 8  10400  17524217.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249775624.
##  3 cycle 2  10400 175242612.
##  4 cycle 3  10400 165064995.
##  5 cycle 4  10400 204685404.
##  6 cycle 5  10400 168192255.
##  7 cycle 6  10400 152406817.
##  8 cycle 7  10400  78147783.
##  9 cycle 8  10400  17100988.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     58468.
## 13 cycle 12 10400     81855.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249385730.
##  3 cycle 2  10400 175414227.
##  4 cycle 3  10400 165756321.
##  5 cycle 4  10400 205081843.
##  6 cycle 5  10400 168551287.
##  7 cycle 6  10400 152485234.
##  8 cycle 7  10400  79362454.
##  9 cycle 8  10400  18039462.
## 10 cycle 9  10400   1005643.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 175626720.
##  4 cycle 3  10400 164669387.
##  5 cycle 4  10400 204685887.
##  6 cycle 5  10400 168222434.
##  7 cycle 6  10400 151230246.
##  8 cycle 7  10400  78694162.
##  9 cycle 8  10400  17291679.
## 10 cycle 9  10400    783466.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 175880299.
##  4 cycle 3  10400 165521226.
##  5 cycle 4  10400 205880416.
##  6 cycle 5  10400 169637848.
##  7 cycle 6  10400 151869241.
##  8 cycle 7  10400  79019017.
##  9 cycle 8  10400  18551092.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    385886.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249897466.
##  3 cycle 2  10400 175675494.
##  4 cycle 3  10400 166259992.
##  5 cycle 4  10400 206451275.
##  6 cycle 5  10400 169885665.
##  7 cycle 6  10400 151642045.
##  8 cycle 7  10400  78688956.
##  9 cycle 8  10400  18308699.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    187096.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250457938.
##  3 cycle 2  10400 175342993.
##  4 cycle 3  10400 164407674.
##  5 cycle 4  10400 205785181.
##  6 cycle 5  10400 169664117.
##  7 cycle 6  10400 153518178.
##  8 cycle 7  10400  81255826.
##  9 cycle 8  10400  18338524.
## 10 cycle 9  10400   1029030.
## 11 cycle 10 10400    210483.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400     11694.
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 175650805.
##  4 cycle 3  10400 165875189.
##  5 cycle 4  10400 204699867.
##  6 cycle 5  10400 167404818.
##  7 cycle 6  10400 149216931.
##  8 cycle 7  10400  78025125.
##  9 cycle 8  10400  18196991.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 175835775.
##  4 cycle 3  10400 164882876.
##  5 cycle 4  10400 205115981.
##  6 cycle 5  10400 168614639.
##  7 cycle 6  10400 150777787.
##  8 cycle 7  10400  78919401.
##  9 cycle 8  10400  16704226.
## 10 cycle 9  10400    795159.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 176684539.
##  4 cycle 3  10400 167007984.
##  5 cycle 4  10400 205715110.
##  6 cycle 5  10400 170216698.
##  7 cycle 6  10400 152812883.
##  8 cycle 7  10400  79224190.
##  9 cycle 8  10400  18515843.
## 10 cycle 9  10400    935481.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     93548.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     23387.
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250311728.
##  3 cycle 2  10400 174899183.
##  4 cycle 3  10400 165446638.
##  5 cycle 4  10400 207860852.
##  6 cycle 5  10400 171285125.
##  7 cycle 6  10400 152786978.
##  8 cycle 7  10400  79523764.
##  9 cycle 8  10400  17338851.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250652885.
##  3 cycle 2  10400 176303670.
##  4 cycle 3  10400 165510685.
##  5 cycle 4  10400 207009293.
##  6 cycle 5  10400 170332604.
##  7 cycle 6  10400 154336817.
##  8 cycle 7  10400  81393349.
##  9 cycle 8  10400  18949921.
## 10 cycle 9  10400    993949.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 175570664.
##  4 cycle 3  10400 165799021.
##  5 cycle 4  10400 206588622.
##  6 cycle 5  10400 170168835.
##  7 cycle 6  10400 152183551.
##  8 cycle 7  10400  79787662.
##  9 cycle 8  10400  17803031.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     23387.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 176997007.
##  4 cycle 3  10400 165626388.
##  5 cycle 4  10400 206980194.
##  6 cycle 5  10400 169658511.
##  7 cycle 6  10400 155788779.
##  8 cycle 7  10400  80601653.
##  9 cycle 8  10400  18100284.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400     81855.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 174453956.
##  4 cycle 3  10400 163020542.
##  5 cycle 4  10400 206164396.
##  6 cycle 5  10400 169585676.
##  7 cycle 6  10400 152011188.
##  8 cycle 7  10400  80347419.
##  9 cycle 8  10400  18201959.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400    187096.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 174977500.
##  4 cycle 3  10400 163173144.
##  5 cycle 4  10400 205258368.
##  6 cycle 5  10400 167890553.
##  7 cycle 6  10400 150773470.
##  8 cycle 7  10400  78274151.
##  9 cycle 8  10400  17277850.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400         0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 175317491.
##  4 cycle 3  10400 164763218.
##  5 cycle 4  10400 204575360.
##  6 cycle 5  10400 167809101.
##  7 cycle 6  10400 150513801.
##  8 cycle 7  10400  78352210.
##  9 cycle 8  10400  17369768.
## 10 cycle 9  10400    993949.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     11694.
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250141149.
##  3 cycle 2  10400 175954773.
##  4 cycle 3  10400 165422125.
##  5 cycle 4  10400 206330669.
##  6 cycle 5  10400 170369238.
##  7 cycle 6  10400 153440922.
##  8 cycle 7  10400  79354273.
##  9 cycle 8  10400  17938146.
## 10 cycle 9  10400    748385.
## 11 cycle 10 10400    175403.
## 12 cycle 11 10400     35081.
## 13 cycle 12 10400     70161.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400         0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249702519.
##  3 cycle 2  10400 174070858.
##  4 cycle 3  10400 164457484.
##  5 cycle 4  10400 205009838.
##  6 cycle 5  10400 169501213.
##  7 cycle 6  10400 150948409.
##  8 cycle 7  10400  79502945.
##  9 cycle 8  10400  17429775.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400    163709.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 176116877.
##  4 cycle 3  10400 165580793.
##  5 cycle 4  10400 207011571.
##  6 cycle 5  10400 168085782.
##  7 cycle 6  10400 152642516.
##  8 cycle 7  10400  80566720.
##  9 cycle 8  10400  18611099.
## 10 cycle 9  10400    947175.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400    105242.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250482306.
##  3 cycle 2  10400 175261029.
##  4 cycle 3  10400 165363879.
##  5 cycle 4  10400 206180481.
##  6 cycle 5  10400 169983072.
##  7 cycle 6  10400 152896065.
##  8 cycle 7  10400  80229224.
##  9 cycle 8  10400  17749800.
## 10 cycle 9  10400    830240.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250189886.
##  3 cycle 2  10400 174554535.
##  4 cycle 3  10400 164964580.
##  5 cycle 4  10400 205129961.
##  6 cycle 5  10400 168387501.
##  7 cycle 6  10400 151686054.
##  8 cycle 7  10400  79256897.
##  9 cycle 8  10400  17747634.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 176213206.
##  4 cycle 3  10400 165815888.
##  5 cycle 4  10400 205176940.
##  6 cycle 5  10400 169276654.
##  7 cycle 6  10400 152406043.
##  8 cycle 7  10400  80472312.
##  9 cycle 8  10400  18039462.
## 10 cycle 9  10400    923788.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     35081.
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249970571.
##  3 cycle 2  10400 176265824.
##  4 cycle 3  10400 166507214.
##  5 cycle 4  10400 206089941.
##  6 cycle 5  10400 168880124.
##  7 cycle 6  10400 152280266.
##  8 cycle 7  10400  78767009.
##  9 cycle 8  10400  17775471.
## 10 cycle 9  10400    841933.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 175750374.
##  4 cycle 3  10400 164538663.
##  5 cycle 4  10400 205241143.
##  6 cycle 5  10400 168190525.
##  7 cycle 6  10400 150543117.
##  8 cycle 7  10400  78355175.
##  9 cycle 8  10400  17524217.
## 10 cycle 9  10400    689918.
## 11 cycle 10 10400    233870.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 175342587.
##  4 cycle 3  10400 162954915.
##  5 cycle 4  10400 205493090.
##  6 cycle 5  10400 169255526.
##  7 cycle 6  10400 154371611.
##  8 cycle 7  10400  80699040.
##  9 cycle 8  10400  18681955.
## 10 cycle 9  10400    958869.
## 11 cycle 10 10400    327419.
## 12 cycle 11 10400    163709.
## 13 cycle 12 10400     81855.
## 14 cycle 13 10400     70161.
## 15 cycle 14 10400     35081.
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250262991.
##  3 cycle 2  10400 176390082.
##  4 cycle 3  10400 165425815.
##  5 cycle 4  10400 205378973.
##  6 cycle 5  10400 167700481.
##  7 cycle 6  10400 151279988.
##  8 cycle 7  10400  78020661.
##  9 cycle 8  10400  16669792.
## 10 cycle 9  10400    760079.
## 11 cycle 10 10400    339112.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400    105242.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250165517.
##  3 cycle 2  10400 175711517.
##  4 cycle 3  10400 164079273.
##  5 cycle 4  10400 205544936.
##  6 cycle 5  10400 169598620.
##  7 cycle 6  10400 153437958.
##  8 cycle 7  10400  79672441.
##  9 cycle 8  10400  17347713.
## 10 cycle 9  10400    631450.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 175961856.
##  4 cycle 3  10400 164686260.
##  5 cycle 4  10400 206512234.
##  6 cycle 5  10400 169268036.
##  7 cycle 6  10400 151961833.
##  8 cycle 7  10400  79479161.
##  9 cycle 8  10400  18291433.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    304031.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249921834.
##  3 cycle 2  10400 176023985.
##  4 cycle 3  10400 166004864.
##  5 cycle 4  10400 206310027.
##  6 cycle 5  10400 168001751.
##  7 cycle 6  10400 152424728.
##  8 cycle 7  10400  79627094.
##  9 cycle 8  10400  18857287.
## 10 cycle 9  10400   1064110.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400         0 
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     23387.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 175661733.
##  4 cycle 3  10400 166283449.
##  5 cycle 4  10400 207288371.
##  6 cycle 5  10400 169063740.
##  7 cycle 6  10400 151918790.
##  8 cycle 7  10400  79593643.
##  9 cycle 8  10400  17997438.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400    105242.
## 14 cycle 13 10400     46774.
## 15 cycle 14 10400     11694.
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 175534641.
##  4 cycle 3  10400 164941387.
##  5 cycle 4  10400 205342246.
##  6 cycle 5  10400 168984867.
##  7 cycle 6  10400 152709141.
##  8 cycle 7  10400  79736369.
##  9 cycle 8  10400  17623546.
## 10 cycle 9  10400    631450.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 176164838.
##  4 cycle 3  10400 166368578.
##  5 cycle 4  10400 205589947.
##  6 cycle 5  10400 167552650.
##  7 cycle 6  10400 150810392.
##  8 cycle 7  10400  78547716.
##  9 cycle 8  10400  18352532.
## 10 cycle 9  10400    806853.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     11694.
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 176907356.
##  4 cycle 3  10400 165134844.
##  5 cycle 4  10400 207026034.
##  6 cycle 5  10400 169908940.
##  7 cycle 6  10400 152320345.
##  8 cycle 7  10400  79630814.
##  9 cycle 8  10400  18240824.
## 10 cycle 9  10400    993949.
## 11 cycle 10 10400    397580.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249605045.
##  3 cycle 2  10400 175293813.
##  4 cycle 3  10400 164423486.
##  5 cycle 4  10400 205350703.
##  6 cycle 5  10400 167996993.
##  7 cycle 6  10400 152097079.
##  8 cycle 7  10400  80000267.
##  9 cycle 8  10400  17242243.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250092412.
##  3 cycle 2  10400 176012651.
##  4 cycle 3  10400 164313844.
##  5 cycle 4  10400 205699991.
##  6 cycle 5  10400 169422772.
##  7 cycle 6  10400 150883525.
##  8 cycle 7  10400  79169921.
##  9 cycle 8  10400  17490239.
## 10 cycle 9  10400    865320.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400     70161.
## 13 cycle 12 10400     11694.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     11694.
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250068044.
##  3 cycle 2  10400 177024530.
##  4 cycle 3  10400 165607410.
##  5 cycle 4  10400 205109319.
##  6 cycle 5  10400 167996577.
##  7 cycle 6  10400 149840011.
##  8 cycle 7  10400  78588602.
##  9 cycle 8  10400  17643793.
## 10 cycle 9  10400    853627.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249483204.
##  3 cycle 2  10400 176134482.
##  4 cycle 3  10400 165887841.
##  5 cycle 4  10400 206153178.
##  6 cycle 5  10400 169581800.
##  7 cycle 6  10400 151567945.
##  8 cycle 7  10400  80333296.
##  9 cycle 8  10400  17885729.
## 10 cycle 9  10400    982256.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400         0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249994939.
##  3 cycle 2  10400 175282885.
##  4 cycle 3  10400 164066622.
##  5 cycle 4  10400 205530956.
##  6 cycle 5  10400 168019852.
##  7 cycle 6  10400 149703217.
##  8 cycle 7  10400  77597682.
##  9 cycle 8  10400  17873529.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    292338.
## 12 cycle 11 10400     46774.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249824360.
##  3 cycle 2  10400 175837598.
##  4 cycle 3  10400 165686744.
##  5 cycle 4  10400 207289027.
##  6 cycle 5  10400 170495958.
##  7 cycle 6  10400 152412356.
##  8 cycle 7  10400  79717041.
##  9 cycle 8  10400  17452367.
## 10 cycle 9  10400    877014.
## 11 cycle 10 10400    315725.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     93548.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250043676.
##  3 cycle 2  10400 175682578.
##  4 cycle 3  10400 163949074.
##  5 cycle 4  10400 205628815.
##  6 cycle 5  10400 168132764.
##  7 cycle 6  10400 152989237.
##  8 cycle 7  10400  80500557.
##  9 cycle 8  10400  18113478.
## 10 cycle 9  10400    900401.
## 11 cycle 10 10400    257257.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400     11694.
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250116781.
##  3 cycle 2  10400 176919700.
##  4 cycle 3  10400 165827750.
##  5 cycle 4  10400 207815495.
##  6 cycle 5  10400 171303242.
##  7 cycle 6  10400 154010522.
##  8 cycle 7  10400  80416557.
##  9 cycle 8  10400  18767813.
## 10 cycle 9  10400   1145965.
## 11 cycle 10 10400    467741.
## 12 cycle 11 10400    175403.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249848729.
##  3 cycle 2  10400 176247813.
##  4 cycle 3  10400 165141694.
##  5 cycle 4  10400 204418510.
##  6 cycle 5  10400 167894862.
##  7 cycle 6  10400 150657876.
##  8 cycle 7  10400  78289023.
##  9 cycle 8  10400  17242064.
## 10 cycle 9  10400    888707.
## 11 cycle 10 10400    268951.
## 12 cycle 11 10400    140322.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     35081.
## 15 cycle 14 10400     23387.
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249653782.
##  3 cycle 2  10400 175451667.
##  4 cycle 3  10400 164219488.
##  5 cycle 4  10400 205321121.
##  6 cycle 5  10400 169396037.
##  7 cycle 6  10400 152603791.
##  8 cycle 7  10400  78703820.
##  9 cycle 8  10400  17607730.
## 10 cycle 9  10400    993949.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250214254.
##  3 cycle 2  10400 176491068.
##  4 cycle 3  10400 165844882.
##  5 cycle 4  10400 206922169.
##  6 cycle 5  10400 170099860.
##  7 cycle 6  10400 153779395.
##  8 cycle 7  10400  80099872.
##  9 cycle 8  10400  17809807.
## 10 cycle 9  10400    724998.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400    128629.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400         0 
## 15 cycle 14 10400     23387.
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249946202.
##  3 cycle 2  10400 175460167.
##  4 cycle 3  10400 165674883.
##  5 cycle 4  10400 206449653.
##  6 cycle 5  10400 170344250.
##  7 cycle 6  10400 153924437.
##  8 cycle 7  10400  80927253.
##  9 cycle 8  10400  17380339.
## 10 cycle 9  10400    608063.
## 11 cycle 10 10400    222177.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     46774.
## 14 cycle 13 10400     70161.
## 15 cycle 14 10400     11694.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249556309.
##  3 cycle 2  10400 176124565.
##  4 cycle 3  10400 165676463.
##  5 cycle 4  10400 205506241.
##  6 cycle 5  10400 167762985.
##  7 cycle 6  10400 150654719.
##  8 cycle 7  10400  79023479.
##  9 cycle 8  10400  17862143.
## 10 cycle 9  10400    912094.
## 11 cycle 10 10400    198790.
## 12 cycle 11 10400    116935.
## 13 cycle 12 10400     23387.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400         0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 250409201.
##  3 cycle 2  10400 176741407.
##  4 cycle 3  10400 165535457.
##  5 cycle 4  10400 205686184.
##  6 cycle 5  10400 168428876.
##  7 cycle 6  10400 151081273.
##  8 cycle 7  10400  78465940.
##  9 cycle 8  10400  17884736.
## 10 cycle 9  10400    771772.
## 11 cycle 10 10400    350806.
## 12 cycle 11 10400     93548.
## 13 cycle 12 10400     58468.
## 14 cycle 13 10400     11694.
## 15 cycle 14 10400         0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n  sum_costs
##    <fct>    <int>      <dbl>
##  1 cycle 0  10400 261295475.
##  2 cycle 1  10400 249799992.
##  3 cycle 2  10400 176330787.
##  4 cycle 3  10400 166038073.
##  5 cycle 4  10400 206315550.
##  6 cycle 5  10400 168659458.
##  7 cycle 6  10400 152612039.
##  8 cycle 7  10400  79978710.
##  9 cycle 8  10400  17845334.
## 10 cycle 9  10400    736692.
## 11 cycle 10 10400    245564.
## 12 cycle 11 10400    152016.
## 13 cycle 12 10400     35081.
## 14 cycle 13 10400     23387.
## 15 cycle 14 10400     11694.

The variability of costs over 30 simulations is observed through a box plot:

#Males
final_cost_m2_alt_combinedB <- bind_rows(final_cost_m2_altB)

final_cost_m2_alt_combinedB$cycle <- factor(final_cost_m2_alt_combinedB$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_m_altB <- ggplot(final_cost_m2_alt_combinedB, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Males)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m_altB

#Females
final_cost_f2_alt_combinedB <- bind_rows(final_cost_f2_altB)

final_cost_f2_alt_combinedB$cycle <- factor(final_cost_f2_alt_combinedB$cycle, 
                                           levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", 
                                                      "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))

var_graph_f_altB <- ggplot(final_cost_f2_alt_combinedB, aes(x = cycle, y = sum_costs)) +
  geom_boxplot(width = 0.9) +  
  labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Females)",
       x = "Cycle",
       y = "Variability") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f_altB

The graphs showcasing costs over cycles are:

#Averaging costs across simulations
#Males
combined_costs_m_altB <- map_df(final_cost_m2_altB, ~ .x)
mean_costs_per_cycle_m_altB <- combined_costs_m_altB %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_m_altB)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0     440865997.
##  2 cycle 1     284876172.
##  3 cycle 2     242888182.
##  4 cycle 3     285302758.
##  5 cycle 4     260722484.
##  6 cycle 5     200923111.
##  7 cycle 6     134929235.
##  8 cycle 7      56323211.
##  9 cycle 8      19790102.
## 10 cycle 9       9537283.
## 11 cycle 10      3719479.
## 12 cycle 11      1438244.
## 13 cycle 12       559409.
## 14 cycle 13       216340.
## 15 cycle 14        85418.
#Females
combined_costs_f_altB <- map_df(final_cost_f2_altB, ~ .x)
mean_costs_per_cycle_f_altB <- combined_costs_f_altB %>%
  group_by(cycle) %>%
  summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
  mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
  arrange(cycle)
print(mean_costs_per_cycle_f_altB)
## # A tibble: 15 × 2
##    cycle    avg_tot_costs
##    <fct>            <dbl>
##  1 cycle 0     261295475.
##  2 cycle 1     249974470.
##  3 cycle 2     175869911.
##  4 cycle 3     165329375.
##  5 cycle 4     205948998.
##  6 cycle 5     169152346.
##  7 cycle 6     152042009.
##  8 cycle 7      79282562.
##  9 cycle 8      17711338.
## 10 cycle 9        830006.
## 11 cycle 10       268951.
## 12 cycle 11       104423.
## 13 cycle 12        45956.
## 14 cycle 13        24089.
## 15 cycle 14        10524.
#Graphs
#Males
graph1_altB <- ggplot(data = mean_costs_per_cycle_m_altB %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "turquoise") +
  ggtitle("Average total costs from microsimulation, alternative scenario B (Males)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m_alt$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
  options(scipen=999)
  
#Females
graph2_altB <- ggplot(data = mean_costs_per_cycle_f_altB %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
  geom_col(fill = "pink") +
  ggtitle("Average total costs from microsimulation, alternative scenario B (Females)") +
  xlab("Year") +
  ylab("Cost") +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f_alt$avg_tot_costs) * 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
  options(scipen=999)

graph1_altB

graph2_altB

Let’s compare graphs across scenarios:

#Males
mean_costs_combined_mB <- mean_costs_per_cycle_m %>%
  rename(avg_tot_costs_baseline = avg_tot_costs) %>%
  inner_join(mean_costs_per_cycle_m_altB %>%
               rename(avg_tot_costs_alt = avg_tot_costs),
             by = "cycle") %>%
  mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>% 
  pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
               names_to = "Scenario", values_to = "avg_tot_costs") %>%
  mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative B", "extra_cost" = "Extra cost of baseline")) %>% 
  filter(Scenario != "Baseline") %>% 
  mutate(
    Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
  )

graph_combined_mB <- ggplot(data = mean_costs_combined_mB, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
  geom_col(data = subset(mean_costs_combined_mB, Scenario == "Alternative B"), fill = "blue", width = 0.4) +
  geom_col(data = subset(mean_costs_combined_mB, Scenario == "Extra cost of baseline"),
           aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")), 
           width = 0.4) +
  scale_fill_manual(name = "Gains/losses", values = c("Alternative B" = "blue", "Loss" = "red", "Gain" = "green")) +
  ggtitle("Comparison of average total costs of alternative scenario B wrt baseline scenario (Males)") +
  xlab("Cycle") +
  ylab("Cost") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
  scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_m$avg_tot_costs), max(mean_costs_combined_m$avg_tot_costs)))
graph_combined_mB

#Females
mean_costs_combined_fB <- mean_costs_per_cycle_f %>%
  rename(avg_tot_costs_baseline = avg_tot_costs) %>%
  inner_join(mean_costs_per_cycle_f_altB %>%
               rename(avg_tot_costs_alt = avg_tot_costs),
             by = "cycle") %>%
  mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>% 
  pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
               names_to = "Scenario", values_to = "avg_tot_costs") %>%
  mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative B", "extra_cost" = "Extra cost of baseline")) %>% 
  filter(Scenario != "Baseline") %>% 
  mutate(
    Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
  )

graph_combined_fB <- ggplot(data = mean_costs_combined_fB, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
  geom_col(data = subset(mean_costs_combined_fB, Scenario == "Alternative B"), fill = "pink", width = 0.4) +
  geom_col(data = subset(mean_costs_combined_fB, Scenario == "Extra cost of baseline"),
           aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")), 
           width = 0.4) +
  scale_fill_manual(name = "Gains/losses", values = c("Alternative B" = "pink", "Loss" = "red", "Gain" = "green")) +
  ggtitle("Comparison of average total costs of alternative scenario B wrt baseline scenario (Females)") +
  xlab("Cycle") +
  ylab("Cost") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
  scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_f$avg_tot_costs), max(mean_costs_combined_f$avg_tot_costs)))
graph_combined_fB

Discounted costs are:

discounted_costs_m_altB <-
  map(final_cost_m2_altB, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_m_altB
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       189677147.
##  4 cycle 3  15600       198752576.
##  5 cycle 4  15600       160218901.
##  6 cycle 5  15600       109048665.
##  7 cycle 6  15600        65115532.
##  8 cycle 7  15600        23542292.
##  9 cycle 8  15600         7282837.
## 10 cycle 9  15600         3123446.
## 11 cycle 10 15600         1871706.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          236716.
## 14 cycle 13 15600           70025.
## 15 cycle 14 15600           26897.
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       189051187.
##  4 cycle 3  15600       197323674.
##  5 cycle 4  15600       159181160.
##  6 cycle 5  15600       108586760.
##  7 cycle 6  15600        65061158.
##  8 cycle 7  15600        24304360.
##  9 cycle 8  15600         7478882.
## 10 cycle 9  15600         3184116.
## 11 cycle 10 15600         1829442.
## 12 cycle 11 15600          661345.
## 13 cycle 12 15600          234114.
## 14 cycle 13 15600           70025.
## 15 cycle 14 15600           17931.
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       189144960.
##  4 cycle 3  15600       196884660.
##  5 cycle 4  15600       160077853.
##  6 cycle 5  15600       108970547.
##  7 cycle 6  15600        64982692.
##  8 cycle 7  15600        23864795.
##  9 cycle 8  15600         7609362.
## 10 cycle 9  15600         3318008.
## 11 cycle 10 15600         1853593.
## 12 cycle 11 15600          739809.
## 13 cycle 12 15600          270532.
## 14 cycle 13 15600           91757.
## 15 cycle 14 15600           31380.
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252006927.
##  3 cycle 2  15600       188747190.
##  4 cycle 3  15600       197252388.
##  5 cycle 4  15600       159772648.
##  6 cycle 5  15600       108942781.
##  7 cycle 6  15600        64681507.
##  8 cycle 7  15600        23999914.
##  9 cycle 8  15600         7434164.
## 10 cycle 9  15600         3284535.
## 11 cycle 10 15600         1835480.
## 12 cycle 11 15600          689368.
## 13 cycle 12 15600          254925.
## 14 cycle 13 15600           82098.
## 15 cycle 14 15600           29139.
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       190661508.
##  4 cycle 3  15600       196616186.
##  5 cycle 4  15600       158012804.
##  6 cycle 5  15600       107971674.
##  7 cycle 6  15600        64005132.
##  8 cycle 7  15600        23859027.
##  9 cycle 8  15600         7368741.
## 10 cycle 9  15600         3336836.
## 11 cycle 10 15600         1756989.
## 12 cycle 11 15600          672554.
## 13 cycle 12 15600          270532.
## 14 cycle 13 15600           82098.
## 15 cycle 14 15600           35863.
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251973685.
##  3 cycle 2  15600       190009684.
##  4 cycle 3  15600       197853535.
##  5 cycle 4  15600       161209355.
##  6 cycle 5  15600       109926916.
##  7 cycle 6  15600        65292077.
##  8 cycle 7  15600        24114001.
##  9 cycle 8  15600         7508466.
## 10 cycle 9  15600         3278258.
## 11 cycle 10 15600         1835480.
## 12 cycle 11 15600          700577.
## 13 cycle 12 15600          239317.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           29139.
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252605281.
##  3 cycle 2  15600       190342222.
##  4 cycle 3  15600       198261110.
##  5 cycle 4  15600       160801263.
##  6 cycle 5  15600       108964709.
##  7 cycle 6  15600        64908947.
##  8 cycle 7  15600        24200085.
##  9 cycle 8  15600         7569099.
## 10 cycle 9  15600         3112986.
## 11 cycle 10 15600         1753970.
## 12 cycle 11 15600          610903.
## 13 cycle 12 15600          226311.
## 14 cycle 13 15600          103830.
## 15 cycle 14 15600           31380.
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       189169295.
##  4 cycle 3  15600       196425359.
##  5 cycle 4  15600       158461557.
##  6 cycle 5  15600       108869105.
##  7 cycle 6  15600        64851589.
##  8 cycle 7  15600        23947843.
##  9 cycle 8  15600         7507321.
## 10 cycle 9  15600         3117170.
## 11 cycle 10 15600         1850574.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          205500.
## 14 cycle 13 15600           53122.
## 15 cycle 14 15600           20173.
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       187929861.
##  4 cycle 3  15600       195361005.
##  5 cycle 4  15600       158714649.
##  6 cycle 5  15600       108306793.
##  7 cycle 6  15600        64958354.
##  8 cycle 7  15600        24364348.
##  9 cycle 8  15600         7566266.
## 10 cycle 9  15600         3238509.
## 11 cycle 10 15600         1769064.
## 12 cycle 11 15600          596892.
## 13 cycle 12 15600          210703.
## 14 cycle 13 15600           72440.
## 15 cycle 14 15600           29139.
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252405830.
##  3 cycle 2  15600       190852239.
##  4 cycle 3  15600       197358017.
##  5 cycle 4  15600       159223303.
##  6 cycle 5  15600       109075043.
##  7 cycle 6  15600        64849847.
##  8 cycle 7  15600        23852944.
##  9 cycle 8  15600         7356471.
## 10 cycle 9  15600         3152735.
## 11 cycle 10 15600         1657366.
## 12 cycle 11 15600          582880.
## 13 cycle 12 15600          202899.
## 14 cycle 13 15600           60366.
## 15 cycle 14 15600           15690.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       190748401.
##  4 cycle 3  15600       198496852.
##  5 cycle 4  15600       160698839.
##  6 cycle 5  15600       109579092.
##  7 cycle 6  15600        65683888.
##  8 cycle 7  15600        24075604.
##  9 cycle 8  15600         7393600.
## 10 cycle 9  15600         3016751.
## 11 cycle 10 15600         1705668.
## 12 cycle 11 15600          608101.
## 13 cycle 12 15600          189893.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           33621.
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       191054055.
##  4 cycle 3  15600       197919027.
##  5 cycle 4  15600       159702109.
##  6 cycle 5  15600       109341640.
##  7 cycle 6  15600        64852831.
##  8 cycle 7  15600        23971657.
##  9 cycle 8  15600         7459579.
## 10 cycle 9  15600         3081605.
## 11 cycle 10 15600         1747932.
## 12 cycle 11 15600          622112.
## 13 cycle 12 15600          252323.
## 14 cycle 13 15600          111074.
## 15 cycle 14 15600           42587.
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251292226.
##  3 cycle 2  15600       189559547.
##  4 cycle 3  15600       197418296.
##  5 cycle 4  15600       160053757.
##  6 cycle 5  15600       107830492.
##  7 cycle 6  15600        64067954.
##  8 cycle 7  15600        23656950.
##  9 cycle 8  15600         7353893.
## 10 cycle 9  15600         3108802.
## 11 cycle 10 15600         1672460.
## 12 cycle 11 15600          605298.
## 13 cycle 12 15600          200298.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           31380.
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252306104.
##  3 cycle 2  15600       190288965.
##  4 cycle 3  15600       198332396.
##  5 cycle 4  15600       158233199.
##  6 cycle 5  15600       108021368.
##  7 cycle 6  15600        64110663.
##  8 cycle 7  15600        23165885.
##  9 cycle 8  15600         7127420.
## 10 cycle 9  15600         3018843.
## 11 cycle 10 15600         1663404.
## 12 cycle 11 15600          563264.
## 13 cycle 12 15600          176886.
## 14 cycle 13 15600           65196.
## 15 cycle 14 15600           17931.
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       190245135.
##  4 cycle 3  15600       196169780.
##  5 cycle 4  15600       159141487.
##  6 cycle 5  15600       108302685.
##  7 cycle 6  15600        64004385.
##  8 cycle 7  15600        23457848.
##  9 cycle 8  15600         7386644.
## 10 cycle 9  15600         3110894.
## 11 cycle 10 15600         1738876.
## 12 cycle 11 15600          652938.
## 13 cycle 12 15600          213304.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           35863.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       189762511.
##  4 cycle 3  15600       197175163.
##  5 cycle 4  15600       159766167.
##  6 cycle 5  15600       109006863.
##  7 cycle 6  15600        64370633.
##  8 cycle 7  15600        23966654.
##  9 cycle 8  15600         7381298.
## 10 cycle 9  15600         3125538.
## 11 cycle 10 15600         1823404.
## 12 cycle 11 15600          543648.
## 13 cycle 12 15600          182089.
## 14 cycle 13 15600           48293.
## 15 cycle 14 15600           20173.
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       190111356.
##  4 cycle 3  15600       196201365.
##  5 cycle 4  15600       157901284.
##  6 cycle 5  15600       108187892.
##  7 cycle 6  15600        64459526.
##  8 cycle 7  15600        23863460.
##  9 cycle 8  15600         7398468.
## 10 cycle 9  15600         3094157.
## 11 cycle 10 15600         1781140.
## 12 cycle 11 15600          599694.
## 13 cycle 12 15600          215905.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           31380.
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       188435674.
##  4 cycle 3  15600       195794951.
##  5 cycle 4  15600       158040357.
##  6 cycle 5  15600       107136594.
##  7 cycle 6  15600        63456389.
##  8 cycle 7  15600        23726823.
##  9 cycle 8  15600         7374741.
## 10 cycle 9  15600         3062776.
## 11 cycle 10 15600         1744913.
## 12 cycle 11 15600          669752.
## 13 cycle 12 15600          236716.
## 14 cycle 13 15600           94172.
## 15 cycle 14 15600           22414.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       190930213.
##  4 cycle 3  15600       197732846.
##  5 cycle 4  15600       159834875.
##  6 cycle 5  15600       108177270.
##  7 cycle 6  15600        64188629.
##  8 cycle 7  15600        23860230.
##  9 cycle 8  15600         7484373.
## 10 cycle 9  15600         3129722.
## 11 cycle 10 15600         1856612.
## 12 cycle 11 15600          692170.
## 13 cycle 12 15600          223709.
## 14 cycle 13 15600          101416.
## 15 cycle 14 15600           29139.
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251724371.
##  3 cycle 2  15600       189047492.
##  4 cycle 3  15600       195684834.
##  5 cycle 4  15600       158715812.
##  6 cycle 5  15600       107038954.
##  7 cycle 6  15600        64068206.
##  8 cycle 7  15600        24258750.
##  9 cycle 8  15600         7695155.
## 10 cycle 9  15600         3305455.
## 11 cycle 10 15600         1856612.
## 12 cycle 11 15600          636124.
## 13 cycle 12 15600          228912.
## 14 cycle 13 15600           74854.
## 15 cycle 14 15600           24656.
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251258984.
##  3 cycle 2  15600       188878292.
##  4 cycle 3  15600       195574573.
##  5 cycle 4  15600       158304232.
##  6 cycle 5  15600       108533291.
##  7 cycle 6  15600        65072823.
##  8 cycle 7  15600        23668984.
##  9 cycle 8  15600         7246552.
## 10 cycle 9  15600         3286627.
## 11 cycle 10 15600         1817366.
## 12 cycle 11 15600          734205.
## 13 cycle 12 15600          254925.
## 14 cycle 13 15600           89342.
## 15 cycle 14 15600           33621.
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       188845676.
##  4 cycle 3  15600       196421598.
##  5 cycle 4  15600       159543622.
##  6 cycle 5  15600       109046620.
##  7 cycle 6  15600        64883623.
##  8 cycle 7  15600        23818869.
##  9 cycle 8  15600         7599972.
## 10 cycle 9  15600         3257338.
## 11 cycle 10 15600         1808310.
## 12 cycle 11 15600          680961.
## 13 cycle 12 15600          260127.
## 14 cycle 13 15600          101416.
## 15 cycle 14 15600           42587.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       188764517.
##  4 cycle 3  15600       196923636.
##  5 cycle 4  15600       160066227.
##  6 cycle 5  15600       109202531.
##  7 cycle 6  15600        64663379.
##  8 cycle 7  15600        24131099.
##  9 cycle 8  15600         7559041.
## 10 cycle 9  15600         3234325.
## 11 cycle 10 15600         1832461.
## 12 cycle 11 15600          672554.
## 13 cycle 12 15600          275735.
## 14 cycle 13 15600           99001.
## 15 cycle 14 15600           24656.
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251674508.
##  3 cycle 2  15600       190071731.
##  4 cycle 3  15600       196105450.
##  5 cycle 4  15600       159655635.
##  6 cycle 5  15600       109042503.
##  7 cycle 6  15600        64417811.
##  8 cycle 7  15600        23950309.
##  9 cycle 8  15600         7472993.
## 10 cycle 9  15600         3255246.
## 11 cycle 10 15600         1986424.
## 12 cycle 11 15600          728600.
## 13 cycle 12 15600          226311.
## 14 cycle 13 15600           91757.
## 15 cycle 14 15600           26897.
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       189775633.
##  4 cycle 3  15600       197324691.
##  5 cycle 4  15600       160731393.
##  6 cycle 5  15600       109232675.
##  7 cycle 6  15600        64311038.
##  8 cycle 7  15600        23575104.
##  9 cycle 8  15600         7428673.
## 10 cycle 9  15600         3135998.
## 11 cycle 10 15600         1802272.
## 12 cycle 11 15600          672554.
## 13 cycle 12 15600          231513.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           38104.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       190159390.
##  4 cycle 3  15600       198228945.
##  5 cycle 4  15600       161146779.
##  6 cycle 5  15600       109301541.
##  7 cycle 6  15600        65068107.
##  8 cycle 7  15600        23746398.
##  9 cycle 8  15600         7470337.
## 10 cycle 9  15600         3225957.
## 11 cycle 10 15600         1832461.
## 12 cycle 11 15600          641728.
## 13 cycle 12 15600          252323.
## 14 cycle 13 15600          125562.
## 15 cycle 14 15600           44829.
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       188656983.
##  4 cycle 3  15600       196382332.
##  5 cycle 4  15600       157838708.
##  6 cycle 5  15600       107286001.
##  7 cycle 6  15600        63420638.
##  8 cycle 7  15600        23252164.
##  9 cycle 8  15600         7222125.
## 10 cycle 9  15600         2995830.
## 11 cycle 10 15600         1660385.
## 12 cycle 11 15600          616508.
## 13 cycle 12 15600          208102.
## 14 cycle 13 15600           67610.
## 15 cycle 14 15600           24656.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251790855.
##  3 cycle 2  15600       189334672.
##  4 cycle 3  15600       197818189.
##  5 cycle 4  15600       157654324.
##  6 cycle 5  15600       107131459.
##  7 cycle 6  15600        63922202.
##  8 cycle 7  15600        23522462.
##  9 cycle 8  15600         7521246.
## 10 cycle 9  15600         3179932.
## 11 cycle 10 15600         1865669.
## 12 cycle 11 15600          650135.
## 13 cycle 12 15600          260127.
## 14 cycle 13 15600          106245.
## 15 cycle 14 15600           26897.
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       189386400.
##  4 cycle 3  15600       197298755.
##  5 cycle 4  15600       159197262.
##  6 cycle 5  15600       109020917.
##  7 cycle 6  15600        64347788.
##  8 cycle 7  15600        24111657.
##  9 cycle 8  15600         7489163.
## 10 cycle 9  15600         3163195.
## 11 cycle 10 15600         1829442.
## 12 cycle 11 15600          571671.
## 13 cycle 12 15600          210703.
## 14 cycle 13 15600           70025.
## 15 cycle 14 15600           20173.
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       192109892.
##  4 cycle 3  15600       198937608.
##  5 cycle 4  15600       159976910.
##  6 cycle 5  15600       107548462.
##  7 cycle 6  15600        62969965.
##  8 cycle 7  15600        23127998.
##  9 cycle 8  15600         7187719.
## 10 cycle 9  15600         2933068.
## 11 cycle 10 15600         1603026.
## 12 cycle 11 15600          568868.
## 13 cycle 12 15600          213304.
## 14 cycle 13 15600           65196.
## 15 cycle 14 15600           29139.
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       190123460.
##  4 cycle 3  15600       196552291.
##  5 cycle 4  15600       159074260.
##  6 cycle 5  15600       107738653.
##  7 cycle 6  15600        64326681.
##  8 cycle 7  15600        23673356.
##  9 cycle 8  15600         7387822.
## 10 cycle 9  15600         3140182.
## 11 cycle 10 15600         1787178.
## 12 cycle 11 15600          661345.
## 13 cycle 12 15600          236716.
## 14 cycle 13 15600           74854.
## 15 cycle 14 15600           31380.
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       188061602.
##  4 cycle 3  15600       197027232.
##  5 cycle 4  15600       157988913.
##  6 cycle 5  15600       108006971.
##  7 cycle 6  15600        64212963.
##  8 cycle 7  15600        24083849.
##  9 cycle 8  15600         7415194.
## 10 cycle 9  15600         3092065.
## 11 cycle 10 15600         1756989.
## 12 cycle 11 15600          619310.
## 13 cycle 12 15600          221108.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           29139.
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       189432139.
##  4 cycle 3  15600       197222836.
##  5 cycle 4  15600       159941919.
##  6 cycle 5  15600       109372136.
##  7 cycle 6  15600        63938093.
##  8 cycle 7  15600        23831851.
##  9 cycle 8  15600         7350504.
## 10 cycle 9  15600         3110894.
## 11 cycle 10 15600         1744913.
## 12 cycle 11 15600          602496.
## 13 cycle 12 15600          210703.
## 14 cycle 13 15600           99001.
## 15 cycle 14 15600           47070.
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       190600861.
##  4 cycle 3  15600       196666750.
##  5 cycle 4  15600       159506448.
##  6 cycle 5  15600       107984016.
##  7 cycle 6  15600        63499094.
##  8 cycle 7  15600        23487939.
##  9 cycle 8  15600         7119495.
## 10 cycle 9  15600         2989554.
## 11 cycle 10 15600         1633215.
## 12 cycle 11 15600          588485.
## 13 cycle 12 15600          215905.
## 14 cycle 13 15600           89342.
## 15 cycle 14 15600           35863.
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       189477497.
##  4 cycle 3  15600       196464335.
##  5 cycle 4  15600       159113790.
##  6 cycle 5  15600       109262486.
##  7 cycle 6  15600        63788619.
##  8 cycle 7  15600        23922501.
##  9 cycle 8  15600         7547584.
## 10 cycle 9  15600         3188300.
## 11 cycle 10 15600         1681517.
## 12 cycle 11 15600          613705.
## 13 cycle 12 15600          234114.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           31380.
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       189148145.
##  4 cycle 3  15600       197345993.
##  5 cycle 4  15600       159477877.
##  6 cycle 5  15600       107196901.
##  7 cycle 6  15600        63730756.
##  8 cycle 7  15600        23280980.
##  9 cycle 8  15600         7246140.
## 10 cycle 9  15600         3092065.
## 11 cycle 10 15600         1723781.
## 12 cycle 11 15600          585682.
## 13 cycle 12 15600          215905.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           33621.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       188620544.
##  4 cycle 3  15600       195678895.
##  5 cycle 4  15600       159156920.
##  6 cycle 5  15600       108444200.
##  7 cycle 6  15600        63956716.
##  8 cycle 7  15600        23394884.
##  9 cycle 8  15600         7303652.
## 10 cycle 9  15600         3211313.
## 11 cycle 10 15600         1714725.
## 12 cycle 11 15600          624915.
## 13 cycle 12 15600          228912.
## 14 cycle 13 15600           82098.
## 15 cycle 14 15600           26897.
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       190185636.
##  4 cycle 3  15600       197532900.
##  5 cycle 4  15600       158978524.
##  6 cycle 5  15600       108488408.
##  7 cycle 6  15600        64891810.
##  8 cycle 7  15600        24015239.
##  9 cycle 8  15600         7457923.
## 10 cycle 9  15600         3159011.
## 11 cycle 10 15600         1772083.
## 12 cycle 11 15600          644531.
## 13 cycle 12 15600          262728.
## 14 cycle 13 15600          123147.
## 15 cycle 14 15600           42587.
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252389209.
##  3 cycle 2  15600       190016564.
##  4 cycle 3  15600       198436573.
##  5 cycle 4  15600       160223552.
##  6 cycle 5  15600       108316730.
##  7 cycle 6  15600        64037904.
##  8 cycle 7  15600        22972612.
##  9 cycle 8  15600         7180096.
## 10 cycle 9  15600         3052316.
## 11 cycle 10 15600         1756989.
## 12 cycle 11 15600          636124.
## 13 cycle 12 15600          226311.
## 14 cycle 13 15600           60366.
## 15 cycle 14 15600           29139.
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251225742.
##  3 cycle 2  15600       189635993.
##  4 cycle 3  15600       196738471.
##  5 cycle 4  15600       160695032.
##  6 cycle 5  15600       108043962.
##  7 cycle 6  15600        63852917.
##  8 cycle 7  15600        23732202.
##  9 cycle 8  15600         7376030.
## 10 cycle 9  15600         3041856.
## 11 cycle 10 15600         1723781.
## 12 cycle 11 15600          624915.
## 13 cycle 12 15600          179488.
## 14 cycle 13 15600           62781.
## 15 cycle 14 15600           22414.
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       190527091.
##  4 cycle 3  15600       196474485.
##  5 cycle 4  15600       159149944.
##  6 cycle 5  15600       108587084.
##  7 cycle 6  15600        64080114.
##  8 cycle 7  15600        23848949.
##  9 cycle 8  15600         7519511.
## 10 cycle 9  15600         3075329.
## 11 cycle 10 15600         1675479.
## 12 cycle 11 15600          582880.
## 13 cycle 12 15600          218507.
## 14 cycle 13 15600           74854.
## 15 cycle 14 15600           26897.
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251957064.
##  3 cycle 2  15600       189618666.
##  4 cycle 3  15600       196219328.
##  5 cycle 4  15600       160098924.
##  6 cycle 5  15600       109139810.
##  7 cycle 6  15600        63862611.
##  8 cycle 7  15600        23166262.
##  9 cycle 8  15600         7246330.
## 10 cycle 9  15600         3016751.
## 11 cycle 10 15600         1642271.
## 12 cycle 11 15600          574473.
## 13 cycle 12 15600          254925.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           26897.
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       190523397.
##  4 cycle 3  15600       199115381.
##  5 cycle 4  15600       159926311.
##  6 cycle 5  15600       108274586.
##  7 cycle 6  15600        64720486.
##  8 cycle 7  15600        23731133.
##  9 cycle 8  15600         7532370.
## 10 cycle 9  15600         3167379.
## 11 cycle 10 15600         1835480.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          262728.
## 14 cycle 13 15600           91757.
## 15 cycle 14 15600           44829.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       189459532.
##  4 cycle 3  15600       196479553.
##  5 cycle 4  15600       159088706.
##  6 cycle 5  15600       108587426.
##  7 cycle 6  15600        63709158.
##  8 cycle 7  15600        23202182.
##  9 cycle 8  15600         7113861.
## 10 cycle 9  15600         3089973.
## 11 cycle 10 15600         1775102.
## 12 cycle 11 15600          644531.
## 13 cycle 12 15600          252323.
## 14 cycle 13 15600           62781.
## 15 cycle 14 15600           15690.
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       188688071.
##  4 cycle 3  15600       197269929.
##  5 cycle 4  15600       157589104.
##  6 cycle 5  15600       107414155.
##  7 cycle 6  15600        64004632.
##  8 cycle 7  15600        23241198.
##  9 cycle 8  15600         7248240.
## 10 cycle 9  15600         3077421.
## 11 cycle 10 15600         1705668.
## 12 cycle 11 15600          591287.
## 13 cycle 12 15600          215905.
## 14 cycle 13 15600           57952.
## 15 cycle 14 15600           24656.
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251491677.
##  3 cycle 2  15600       189696640.
##  4 cycle 3  15600       196318292.
##  5 cycle 4  15600       159430765.
##  6 cycle 5  15600       107973394.
##  7 cycle 6  15600        63870064.
##  8 cycle 7  15600        23989714.
##  9 cycle 8  15600         7431697.
## 10 cycle 9  15600         3163195.
## 11 cycle 10 15600         1817366.
## 12 cycle 11 15600          697775.
## 13 cycle 12 15600          252323.
## 14 cycle 13 15600           99001.
## 15 cycle 14 15600           42587.
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251624645.
##  3 cycle 2  15600       190754643.
##  4 cycle 3  15600       197599553.
##  5 cycle 4  15600       158266884.
##  6 cycle 5  15600       108573048.
##  7 cycle 6  15600        64094767.
##  8 cycle 7  15600        23830454.
##  9 cycle 8  15600         7384687.
## 10 cycle 9  15600         3117170.
## 11 cycle 10 15600         1790197.
## 12 cycle 11 15600          599694.
## 13 cycle 12 15600          234114.
## 14 cycle 13 15600           70025.
## 15 cycle 14 15600           26897.
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       189136551.
##  4 cycle 3  15600       196550984.
##  5 cycle 4  15600       158796815.
##  6 cycle 5  15600       107485111.
##  7 cycle 6  15600        64178454.
##  8 cycle 7  15600        23903496.
##  9 cycle 8  15600         7463302.
## 10 cycle 9  15600         3297087.
## 11 cycle 10 15600         1741894.
## 12 cycle 11 15600          650135.
## 13 cycle 12 15600          223709.
## 14 cycle 13 15600           74854.
## 15 cycle 14 15600           29139.
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       190542381.
##  4 cycle 3  15600       197429594.
##  5 cycle 4  15600       159487846.
##  6 cycle 5  15600       108013124.
##  7 cycle 6  15600        64329408.
##  8 cycle 7  15600        23747722.
##  9 cycle 8  15600         7489051.
## 10 cycle 9  15600         3242693.
## 11 cycle 10 15600         1880763.
## 12 cycle 11 15600          669752.
## 13 cycle 12 15600          257526.
## 14 cycle 13 15600           91757.
## 15 cycle 14 15600           26897.
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251391952.
##  3 cycle 2  15600       189749897.
##  4 cycle 3  15600       195866527.
##  5 cycle 4  15600       158323647.
##  6 cycle 5  15600       107509075.
##  7 cycle 6  15600        64108921.
##  8 cycle 7  15600        23753744.
##  9 cycle 8  15600         7297573.
## 10 cycle 9  15600         3238509.
## 11 cycle 10 15600         1874725.
## 12 cycle 11 15600          608101.
## 13 cycle 12 15600          218507.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           24656.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252090032.
##  3 cycle 2  15600       189768372.
##  4 cycle 3  15600       196634439.
##  5 cycle 4  15600       158293449.
##  6 cycle 5  15600       108206748.
##  7 cycle 6  15600        64496272.
##  8 cycle 7  15600        24565345.
##  9 cycle 8  15600         7533292.
## 10 cycle 9  15600         3096249.
## 11 cycle 10 15600         1690573.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          223709.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           33621.
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       189455837.
##  4 cycle 3  15600       197408872.
##  5 cycle 4  15600       160814865.
##  6 cycle 5  15600       109354316.
##  7 cycle 6  15600        65219813.
##  8 cycle 7  15600        24005804.
##  9 cycle 8  15600         7566187.
## 10 cycle 9  15600         3140182.
## 11 cycle 10 15600         1760008.
## 12 cycle 11 15600          605298.
## 13 cycle 12 15600          241918.
## 14 cycle 13 15600           67610.
## 15 cycle 14 15600           26897.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       188643860.
##  4 cycle 3  15600       196057631.
##  5 cycle 4  15600       159357087.
##  6 cycle 5  15600       108462714.
##  7 cycle 6  15600        64527560.
##  8 cycle 7  15600        23760836.
##  9 cycle 8  15600         7391434.
## 10 cycle 9  15600         3129722.
## 11 cycle 10 15600         1772083.
## 12 cycle 11 15600          678158.
## 13 cycle 12 15600          273133.
## 14 cycle 13 15600           94172.
## 15 cycle 14 15600           31380.
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251807476.
##  3 cycle 2  15600       188569581.
##  4 cycle 3  15600       195410843.
##  5 cycle 4  15600       159070309.
##  6 cycle 5  15600       108678256.
##  7 cycle 6  15600        63986765.
##  8 cycle 7  15600        23836793.
##  9 cycle 8  15600         7294883.
## 10 cycle 9  15600         3196668.
## 11 cycle 10 15600         1808310.
## 12 cycle 11 15600          686565.
## 13 cycle 12 15600          195095.
## 14 cycle 13 15600           70025.
## 15 cycle 14 15600           22414.
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251657887.
##  3 cycle 2  15600       190283103.
##  4 cycle 3  15600       196899007.
##  5 cycle 4  15600       157335693.
##  6 cycle 5  15600       107882916.
##  7 cycle 6  15600        64500242.
##  8 cycle 7  15600        23445049.
##  9 cycle 8  15600         7276901.
## 10 cycle 9  15600         3043948.
## 11 cycle 10 15600         1741894.
## 12 cycle 11 15600          655740.
## 13 cycle 12 15600          208102.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           31380.
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252422451.
##  3 cycle 2  15600       190832746.
##  4 cycle 3  15600       198473662.
##  5 cycle 4  15600       160192192.
##  6 cycle 5  15600       109495849.
##  7 cycle 6  15600        65103620.
##  8 cycle 7  15600        23721941.
##  9 cycle 8  15600         7126530.
## 10 cycle 9  15600         3104617.
## 11 cycle 10 15600         1820385.
## 12 cycle 11 15600          641728.
## 13 cycle 12 15600          226311.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           20173.
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251774234.
##  3 cycle 2  15600       190794778.
##  4 cycle 3  15600       197765737.
##  5 cycle 4  15600       159359556.
##  6 cycle 5  15600       109222378.
##  7 cycle 6  15600        64594846.
##  8 cycle 7  15600        23335140.
##  9 cycle 8  15600         7486394.
## 10 cycle 9  15600         3048132.
## 11 cycle 10 15600         1711706.
## 12 cycle 11 15600          599694.
## 13 cycle 12 15600          192494.
## 14 cycle 13 15600           72440.
## 15 cycle 14 15600           29139.
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252173137.
##  3 cycle 2  15600       190451793.
##  4 cycle 3  15600       197881793.
##  5 cycle 4  15600       160213757.
##  6 cycle 5  15600       109772716.
##  7 cycle 6  15600        64922354.
##  8 cycle 7  15600        23778893.
##  9 cycle 8  15600         7513400.
## 10 cycle 9  15600         3244785.
## 11 cycle 10 15600         1886801.
## 12 cycle 11 15600          708984.
## 13 cycle 12 15600          252323.
## 14 cycle 13 15600           94172.
## 15 cycle 14 15600           29139.
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       187693772.
##  4 cycle 3  15600       195311021.
##  5 cycle 4  15600       156721796.
##  6 cycle 5  15600       106855610.
##  7 cycle 6  15600        62940167.
##  8 cycle 7  15600        23145994.
##  9 cycle 8  15600         7319376.
## 10 cycle 9  15600         3037672.
## 11 cycle 10 15600         1687555.
## 12 cycle 11 15600          605298.
## 13 cycle 12 15600          215905.
## 14 cycle 13 15600           82098.
## 15 cycle 14 15600           35863.
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       189903170.
##  4 cycle 3  15600       195103538.
##  5 cycle 4  15600       156432374.
##  6 cycle 5  15600       106959782.
##  7 cycle 6  15600        63593699.
##  8 cycle 7  15600        23632433.
##  9 cycle 8  15600         7462046.
## 10 cycle 9  15600         3215497.
## 11 cycle 10 15600         1841518.
## 12 cycle 11 15600          708984.
## 13 cycle 12 15600          262728.
## 14 cycle 13 15600           86928.
## 15 cycle 14 15600           42587.
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251558161.
##  3 cycle 2  15600       188316165.
##  4 cycle 3  15600       197576218.
##  5 cycle 4  15600       159531676.
##  6 cycle 5  15600       109080539.
##  7 cycle 6  15600        64440656.
##  8 cycle 7  15600        23823301.
##  9 cycle 8  15600         7286259.
## 10 cycle 9  15600         3119262.
## 11 cycle 10 15600         1829442.
## 12 cycle 11 15600          658542.
## 13 cycle 12 15600          249722.
## 14 cycle 13 15600          106245.
## 15 cycle 14 15600           38104.
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252189758.
##  3 cycle 2  15600       188974232.
##  4 cycle 3  15600       197770951.
##  5 cycle 4  15600       159630407.
##  6 cycle 5  15600       107999421.
##  7 cycle 6  15600        63832066.
##  8 cycle 7  15600        23419268.
##  9 cycle 8  15600         7322432.
## 10 cycle 9  15600         3081605.
## 11 cycle 10 15600         1705668.
## 12 cycle 11 15600          664147.
## 13 cycle 12 15600          239317.
## 14 cycle 13 15600           94172.
## 15 cycle 14 15600           33621.
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252106653.
##  3 cycle 2  15600       190267814.
##  4 cycle 3  15600       197294558.
##  5 cycle 4  15600       158073374.
##  6 cycle 5  15600       106709987.
##  7 cycle 6  15600        63644352.
##  8 cycle 7  15600        23530064.
##  9 cycle 8  15600         7434419.
## 10 cycle 9  15600         3148551.
## 11 cycle 10 15600         1820385.
## 12 cycle 11 15600          652938.
## 13 cycle 12 15600          249722.
## 14 cycle 13 15600          115904.
## 15 cycle 14 15600           42587.
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       189385762.
##  4 cycle 3  15600       197274998.
##  5 cycle 4  15600       157400913.
##  6 cycle 5  15600       106392669.
##  7 cycle 6  15600        63029560.
##  8 cycle 7  15600        23127937.
##  9 cycle 8  15600         7184997.
## 10 cycle 9  15600         3041856.
## 11 cycle 10 15600         1609064.
## 12 cycle 11 15600          577275.
## 13 cycle 12 15600          189893.
## 14 cycle 13 15600           89342.
## 15 cycle 14 15600           31380.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       188569581.
##  4 cycle 3  15600       196535766.
##  5 cycle 4  15600       159201244.
##  6 cycle 5  15600       109004115.
##  7 cycle 6  15600        65316406.
##  8 cycle 7  15600        24026326.
##  9 cycle 8  15600         7574144.
## 10 cycle 9  15600         3106709.
## 11 cycle 10 15600         1660385.
## 12 cycle 11 15600          619310.
## 13 cycle 12 15600          252323.
## 14 cycle 13 15600          103830.
## 15 cycle 14 15600           40346.
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       189258226.
##  4 cycle 3  15600       195541827.
##  5 cycle 4  15600       157011248.
##  6 cycle 5  15600       107398408.
##  7 cycle 6  15600        63825869.
##  8 cycle 7  15600        23840594.
##  9 cycle 8  15600         7316431.
## 10 cycle 9  15600         3110894.
## 11 cycle 10 15600         1717743.
## 12 cycle 11 15600          608101.
## 13 cycle 12 15600          221108.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           29139.
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251691129.
##  3 cycle 2  15600       188584361.
##  4 cycle 3  15600       196084873.
##  5 cycle 4  15600       159087399.
##  6 cycle 5  15600       107966196.
##  7 cycle 6  15600        64327428.
##  8 cycle 7  15600        23367586.
##  9 cycle 8  15600         7181607.
## 10 cycle 9  15600         3100433.
## 11 cycle 10 15600         1799253.
## 12 cycle 11 15600          658542.
## 13 cycle 12 15600          241918.
## 14 cycle 13 15600           96586.
## 15 cycle 14 15600           35863.
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251574782.
##  3 cycle 2  15600       189782004.
##  4 cycle 3  15600       195892608.
##  5 cycle 4  15600       158304726.
##  6 cycle 5  15600       108462362.
##  7 cycle 6  15600        64475165.
##  8 cycle 7  15600        23594618.
##  9 cycle 8  15600         7475604.
## 10 cycle 9  15600         3089973.
## 11 cycle 10 15600         1702649.
## 12 cycle 11 15600          608101.
## 13 cycle 12 15600          228912.
## 14 cycle 13 15600           82098.
## 15 cycle 14 15600           38104.
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       191171907.
##  4 cycle 3  15600       196528098.
##  5 cycle 4  15600       158443130.
##  6 cycle 5  15600       107552580.
##  7 cycle 6  15600        63473518.
##  8 cycle 7  15600        23412237.
##  9 cycle 8  15600         7167539.
## 10 cycle 9  15600         3194576.
## 11 cycle 10 15600         1720762.
## 12 cycle 11 15600          683763.
## 13 cycle 12 15600          247121.
## 14 cycle 13 15600           72440.
## 15 cycle 14 15600           29139.
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251873959.
##  3 cycle 2  15600       188628062.
##  4 cycle 3  15600       196587347.
##  5 cycle 4  15600       158717324.
##  6 cycle 5  15600       109487975.
##  7 cycle 6  15600        65055699.
##  8 cycle 7  15600        24470772.
##  9 cycle 8  15600         7542872.
## 10 cycle 9  15600         3156919.
## 11 cycle 10 15600         1826423.
## 12 cycle 11 15600          638926.
## 13 cycle 12 15600          247121.
## 14 cycle 13 15600           70025.
## 15 cycle 14 15600           22414.
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       188361904.
##  4 cycle 3  15600       195334053.
##  5 cycle 4  15600       157334705.
##  6 cycle 5  15600       108361965.
##  7 cycle 6  15600        63758569.
##  8 cycle 7  15600        23547296.
##  9 cycle 8  15600         7174606.
## 10 cycle 9  15600         3031395.
## 11 cycle 10 15600         1735857.
## 12 cycle 11 15600          596892.
## 13 cycle 12 15600          226311.
## 14 cycle 13 15600           86928.
## 15 cycle 14 15600           29139.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       190823699.
##  4 cycle 3  15600       198285171.
##  5 cycle 4  15600       160604903.
##  6 cycle 5  15600       108310550.
##  7 cycle 6  15600        63316591.
##  8 cycle 7  15600        23541783.
##  9 cycle 8  15600         7232994.
## 10 cycle 9  15600         3165287.
## 11 cycle 10 15600         1850574.
## 12 cycle 11 15600          694972.
## 13 cycle 12 15600          244519.
## 14 cycle 13 15600           86928.
## 15 cycle 14 15600           26897.
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251707750.
##  3 cycle 2  15600       189534959.
##  4 cycle 3  15600       196782080.
##  5 cycle 4  15600       158606123.
##  6 cycle 5  15600       107965863.
##  7 cycle 6  15600        64863515.
##  8 cycle 7  15600        23944298.
##  9 cycle 8  15600         7428418.
## 10 cycle 9  15600         3194576.
## 11 cycle 10 15600         1781140.
## 12 cycle 11 15600          641728.
## 13 cycle 12 15600          231513.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           44829.
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252256241.
##  3 cycle 2  15600       190051347.
##  4 cycle 3  15600       197060994.
##  5 cycle 4  15600       159258326.
##  6 cycle 5  15600       108160486.
##  7 cycle 6  15600        65302252.
##  8 cycle 7  15600        23848256.
##  9 cycle 8  15600         7182196.
## 10 cycle 9  15600         3133906.
## 11 cycle 10 15600         1796234.
## 12 cycle 11 15600          689368.
## 13 cycle 12 15600          247121.
## 14 cycle 13 15600           94172.
## 15 cycle 14 15600           33621.
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252056790.
##  3 cycle 2  15600       189719829.
##  4 cycle 3  15600       197397138.
##  5 cycle 4  15600       158920104.
##  6 cycle 5  15600       108167675.
##  7 cycle 6  15600        64269323.
##  8 cycle 7  15600        23810563.
##  9 cycle 8  15600         7146056.
## 10 cycle 9  15600         3000014.
## 11 cycle 10 15600         1654347.
## 12 cycle 11 15600          577275.
## 13 cycle 12 15600          218507.
## 14 cycle 13 15600           53122.
## 15 cycle 14 15600           17931.
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       188965694.
##  4 cycle 3  15600       196667766.
##  5 cycle 4  15600       157524059.
##  6 cycle 5  15600       109301217.
##  7 cycle 6  15600        64595098.
##  8 cycle 7  15600        23974255.
##  9 cycle 8  15600         7264344.
## 10 cycle 9  15600         3211313.
## 11 cycle 10 15600         1769064.
## 12 cycle 11 15600          557659.
## 13 cycle 12 15600          210703.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           35863.
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251524919.
##  3 cycle 2  15600       190646219.
##  4 cycle 3  15600       198197796.
##  5 cycle 4  15600       158903509.
##  6 cycle 5  15600       108709770.
##  7 cycle 6  15600        64829739.
##  8 cycle 7  15600        24033357.
##  9 cycle 8  15600         7265045.
## 10 cycle 9  15600         3196668.
## 11 cycle 10 15600         1738876.
## 12 cycle 11 15600          624915.
## 13 cycle 12 15600          236716.
## 14 cycle 13 15600           65196.
## 15 cycle 14 15600           24656.
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252023548.
##  3 cycle 2  15600       189322568.
##  4 cycle 3  15600       197311795.
##  5 cycle 4  15600       159706759.
##  6 cycle 5  15600       108933538.
##  7 cycle 6  15600        64054547.
##  8 cycle 7  15600        23630283.
##  9 cycle 8  15600         7375186.
## 10 cycle 9  15600         3207128.
## 11 cycle 10 15600         1847555.
## 12 cycle 11 15600          680961.
## 13 cycle 12 15600          236716.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           35863.
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       191013411.
##  4 cycle 3  15600       197779213.
##  5 cycle 4  15600       158509019.
##  6 cycle 5  15600       108958880.
##  7 cycle 6  15600        64538482.
##  8 cycle 7  15600        23461649.
##  9 cycle 8  15600         7166982.
## 10 cycle 9  15600         3027211.
## 11 cycle 10 15600         1654347.
## 12 cycle 11 15600          591287.
## 13 cycle 12 15600          226311.
## 14 cycle 13 15600           55537.
## 15 cycle 14 15600           13449.
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       250527662.
##  3 cycle 2  15600       189637522.
##  4 cycle 3  15600       196869455.
##  5 cycle 4  15600       157688678.
##  6 cycle 5  15600       107844880.
##  7 cycle 6  15600        64411601.
##  8 cycle 7  15600        23515431.
##  9 cycle 8  15600         7489306.
## 10 cycle 9  15600         3098341.
## 11 cycle 10 15600         1684536.
## 12 cycle 11 15600          644531.
## 13 cycle 12 15600          197697.
## 14 cycle 13 15600           57952.
## 15 cycle 14 15600           17931.
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251740992.
##  3 cycle 2  15600       189663386.
##  4 cycle 3  15600       196216002.
##  5 cycle 4  15600       158936700.
##  6 cycle 5  15600       107829465.
##  7 cycle 6  15600        63954974.
##  8 cycle 7  15600        23405267.
##  9 cycle 8  15600         7195198.
## 10 cycle 9  15600         3104617.
## 11 cycle 10 15600         1832461.
## 12 cycle 11 15600          650135.
## 13 cycle 12 15600          234114.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           40346.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251840717.
##  3 cycle 2  15600       191391306.
##  4 cycle 3  15600       198417303.
##  5 cycle 4  15600       160020915.
##  6 cycle 5  15600       109046602.
##  7 cycle 6  15600        63997931.
##  8 cycle 7  15600        23537289.
##  9 cycle 8  15600         7225148.
## 10 cycle 9  15600         3159011.
## 11 cycle 10 15600         1726800.
## 12 cycle 11 15600          549252.
## 13 cycle 12 15600          205500.
## 14 cycle 13 15600           96586.
## 15 cycle 14 15600           24656.
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251308847.
##  3 cycle 2  15600       190110718.
##  4 cycle 3  15600       197509723.
##  5 cycle 4  15600       158750804.
##  6 cycle 5  15600       107168117.
##  7 cycle 6  15600        64218917.
##  8 cycle 7  15600        23803411.
##  9 cycle 8  15600         7450078.
## 10 cycle 9  15600         3211313.
## 11 cycle 10 15600         1787178.
## 12 cycle 11 15600          610903.
## 13 cycle 12 15600          228912.
## 14 cycle 13 15600           74854.
## 15 cycle 14 15600           26897.
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252239620.
##  3 cycle 2  15600       189593439.
##  4 cycle 3  15600       197692564.
##  5 cycle 4  15600       158894876.
##  6 cycle 5  15600       107765383.
##  7 cycle 6  15600        64453311.
##  8 cycle 7  15600        23561673.
##  9 cycle 8  15600         7310241.
## 10 cycle 9  15600         3094157.
## 11 cycle 10 15600         1781140.
## 12 cycle 11 15600          624915.
## 13 cycle 12 15600          223709.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           15690.
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251890580.
##  3 cycle 2  15600       189327282.
##  4 cycle 3  15600       196769911.
##  5 cycle 4  15600       158890895.
##  6 cycle 5  15600       108044323.
##  7 cycle 6  15600        64625886.
##  8 cycle 7  15600        23689640.
##  9 cycle 8  15600         7360195.
## 10 cycle 9  15600         3127630.
## 11 cycle 10 15600         1738876.
## 12 cycle 11 15600          641728.
## 13 cycle 12 15600          231513.
## 14 cycle 13 15600           72440.
## 15 cycle 14 15600           24656.
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251608024.
##  3 cycle 2  15600       191762193.
##  4 cycle 3  15600       197055781.
##  5 cycle 4  15600       158782513.
##  6 cycle 5  15600       107152703.
##  7 cycle 6  15600        63792836.
##  8 cycle 7  15600        23634084.
##  9 cycle 8  15600         7282025.
## 10 cycle 9  15600         3110894.
## 11 cycle 10 15600         1769064.
## 12 cycle 11 15600          644531.
## 13 cycle 12 15600          213304.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           24656.
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251408573.
##  3 cycle 2  15600       189453161.
##  4 cycle 3  15600       196106611.
##  5 cycle 4  15600       158489429.
##  6 cycle 5  15600       108215297.
##  7 cycle 6  15600        64133751.
##  8 cycle 7  15600        23782561.
##  9 cycle 8  15600         7366895.
## 10 cycle 9  15600         3207128.
## 11 cycle 10 15600         1760008.
## 12 cycle 11 15600          594089.
## 13 cycle 12 15600          221108.
## 14 cycle 13 15600           82098.
## 15 cycle 14 15600           22414.
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       189609238.
##  4 cycle 3  15600       195520814.
##  5 cycle 4  15600       159833393.
##  6 cycle 5  15600       108484984.
##  7 cycle 6  15600        64206505.
##  8 cycle 7  15600        23631485.
##  9 cycle 8  15600         7566553.
## 10 cycle 9  15600         3205036.
## 11 cycle 10 15600         1775102.
## 12 cycle 11 15600          602496.
## 13 cycle 12 15600          171684.
## 14 cycle 13 15600           67610.
## 15 cycle 14 15600           22414.
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251923822.
##  3 cycle 2  15600       190039243.
##  4 cycle 3  15600       196930156.
##  5 cycle 4  15600       158037044.
##  6 cycle 5  15600       107759221.
##  7 cycle 6  15600        64068696.
##  8 cycle 7  15600        23668413.
##  9 cycle 8  15600         7317577.
## 10 cycle 9  15600         3173655.
## 11 cycle 10 15600         1799253.
## 12 cycle 11 15600          655740.
## 13 cycle 12 15600          244519.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           33621.
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       189823158.
##  4 cycle 3  15600       197084026.
##  5 cycle 4  15600       161505434.
##  6 cycle 5  15600       109895726.
##  7 cycle 6  15600        65054452.
##  8 cycle 7  15600        23776671.
##  9 cycle 8  15600         7283092.
## 10 cycle 9  15600         3100433.
## 11 cycle 10 15600         1790197.
## 12 cycle 11 15600          633322.
## 13 cycle 12 15600          241918.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           24656.
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251757613.
##  3 cycle 2  15600       191320594.
##  4 cycle 3  15600       197403949.
##  5 cycle 4  15600       159641858.
##  6 cycle 5  15600       108874240.
##  7 cycle 6  15600        63728776.
##  8 cycle 7  15600        23363846.
##  9 cycle 8  15600         7342514.
## 10 cycle 9  15600         3131814.
## 11 cycle 10 15600         1832461.
## 12 cycle 11 15600          652938.
## 13 cycle 12 15600          218507.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           22414.
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251824097.
##  3 cycle 2  15600       188606402.
##  4 cycle 3  15600       196175284.
##  5 cycle 4  15600       159873385.
##  6 cycle 5  15600       109682941.
##  7 cycle 6  15600        64959105.
##  8 cycle 7  15600        24139211.
##  9 cycle 8  15600         7539705.
## 10 cycle 9  15600         3182024.
## 11 cycle 10 15600         1723781.
## 12 cycle 11 15600          594089.
## 13 cycle 12 15600          228912.
## 14 cycle 13 15600           94172.
## 15 cycle 14 15600           31380.
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       189574965.
##  4 cycle 3  15600       195805233.
##  5 cycle 4  15600       159561555.
##  6 cycle 5  15600       108664887.
##  7 cycle 6  15600        64362690.
##  8 cycle 7  15600        24162271.
##  9 cycle 8  15600         7426207.
## 10 cycle 9  15600         3142275.
## 11 cycle 10 15600         1766045.
## 12 cycle 11 15600          616508.
## 13 cycle 12 15600          223709.
## 14 cycle 13 15600           96586.
## 15 cycle 14 15600           35863.
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252156516.
##  3 cycle 2  15600       189258226.
##  4 cycle 3  15600       196938708.
##  5 cycle 4  15600       158155189.
##  6 cycle 5  15600       108426722.
##  7 cycle 6  15600        64273045.
##  8 cycle 7  15600        23527150.
##  9 cycle 8  15600         7161348.
## 10 cycle 9  15600         3115078.
## 11 cycle 10 15600         1729819.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          241918.
## 14 cycle 13 15600           79684.
## 15 cycle 14 15600           49311.
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251425194.
##  3 cycle 2  15600       190153910.
##  4 cycle 3  15600       197534932.
##  5 cycle 4  15600       159495810.
##  6 cycle 5  15600       108402740.
##  7 cycle 6  15600        63982292.
##  8 cycle 7  15600        23686725.
##  9 cycle 8  15600         7432986.
## 10 cycle 9  15600         3131814.
## 11 cycle 10 15600         1850574.
## 12 cycle 11 15600          672554.
## 13 cycle 12 15600          226311.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           29139.
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251358710.
##  3 cycle 2  15600       188632266.
##  4 cycle 3  15600       195399255.
##  5 cycle 4  15600       157690510.
##  6 cycle 5  15600       108408227.
##  7 cycle 6  15600        65028133.
##  8 cycle 7  15600        24006690.
##  9 cycle 8  15600         7475126.
## 10 cycle 9  15600         3246878.
## 11 cycle 10 15600         1811329.
## 12 cycle 11 15600          650135.
## 13 cycle 12 15600          247121.
## 14 cycle 13 15600           84513.
## 15 cycle 14 15600           38104.
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251940443.
##  3 cycle 2  15600       190853258.
##  4 cycle 3  15600       198502356.
##  5 cycle 4  15600       158341230.
##  6 cycle 5  15600       107357614.
##  7 cycle 6  15600        64238535.
##  8 cycle 7  15600        23672408.
##  9 cycle 8  15600         7239073.
## 10 cycle 9  15600         3135998.
## 11 cycle 10 15600         1763027.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          202899.
## 14 cycle 13 15600           77269.
## 15 cycle 14 15600           24656.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251907201.
##  3 cycle 2  15600       190509764.
##  4 cycle 3  15600       196794394.
##  5 cycle 4  15600       159198918.
##  6 cycle 5  15600       108165964.
##  7 cycle 6  15600        65148809.
##  8 cycle 7  15600        24163157.
##  9 cycle 8  15600         7314920.
## 10 cycle 9  15600         3161103.
## 11 cycle 10 15600         1723781.
## 12 cycle 11 15600          613705.
## 13 cycle 12 15600          202899.
## 14 cycle 13 15600           82098.
## 15 cycle 14 15600           29139.
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       251641266.
##  3 cycle 2  15600       191226821.
##  4 cycle 3  15600       198638117.
##  5 cycle 4  15600       161001719.
##  6 cycle 5  15600       108631634.
##  7 cycle 6  15600        64179934.
##  8 cycle 7  15600        23581941.
##  9 cycle 8  15600         7299483.
## 10 cycle 9  15600         3050224.
## 11 cycle 10 15600         1672460.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          231513.
## 14 cycle 13 15600          101416.
## 15 cycle 14 15600           42587.
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  15600       440865997.
##  2 cycle 1  15600       252206378.
##  3 cycle 2  15600       190313172.
##  4 cycle 3  15600       197938733.
##  5 cycle 4  15600       159592071.
##  6 cycle 5  15600       108923249.
##  7 cycle 6  15600        64756742.
##  8 cycle 7  15600        23586385.
##  9 cycle 8  15600         7228093.
## 10 cycle 9  15600         3046040.
## 11 cycle 10 15600         1741894.
## 12 cycle 11 15600          630519.
## 13 cycle 12 15600          234114.
## 14 cycle 13 15600           96586.
## 15 cycle 14 15600           40346.
# Females
discounted_costs_f_altB <-
  map(final_cost_f2_altB, 
  ~ .x %>%  
   mutate(
    dw = ifelse(row_number() <= 10, 
                (1)/((1+d.c.1)^(row_number()-1)), 
                (1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
    discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs  
  select(cycle, n, discounted_costs) 
  )
discounted_costs_f_altB
## [[1]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       137949320.
##  4 cycle 3  10400       114860311.
##  5 cycle 4  10400       127427364.
##  6 cycle 5  10400        93168157.
##  7 cycle 6  10400        74023017.
##  8 cycle 7  10400        33729219.
##  9 cycle 8  10400         6519554.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[2]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       137622221.
##  4 cycle 3  10400       114803714.
##  5 cycle 4  10400       125804133.
##  6 cycle 5  10400        90862662.
##  7 cycle 6  10400        72240447.
##  8 cycle 7  10400        33370564.
##  9 cycle 8  10400         6815287.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400               0 
## 
## [[3]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       138261242.
##  4 cycle 3  10400       114696890.
##  5 cycle 4  10400       125865433.
##  6 cycle 5  10400        92458403.
##  7 cycle 6  10400        72600377.
##  8 cycle 7  10400        33337047.
##  9 cycle 8  10400         6581590.
## 10 cycle 9  10400          230952.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            4124.
## 
## [[4]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       136224655.
##  4 cycle 3  10400       113876337.
##  5 cycle 4  10400       125869899.
##  6 cycle 5  10400        91095607.
##  7 cycle 6  10400        72953364.
##  8 cycle 7  10400        33088023.
##  9 cycle 8  10400         6406695.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            4124.
## 
## [[5]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       137265556.
##  4 cycle 3  10400       113871970.
##  5 cycle 4  10400       125380172.
##  6 cycle 5  10400        91581956.
##  7 cycle 6  10400        72132963.
##  8 cycle 7  10400        33273772.
##  9 cycle 8  10400         6635411.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[6]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       137088489.
##  4 cycle 3  10400       113772791.
##  5 cycle 4  10400       126320444.
##  6 cycle 5  10400        92050636.
##  7 cycle 6  10400        72805301.
##  8 cycle 7  10400        33571035.
##  9 cycle 8  10400         6407605.
## 10 cycle 9  10400          227103.
## 11 cycle 10 10400           72208.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            8248.
## 
## [[7]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       137585226.
##  4 cycle 3  10400       115153117.
##  5 cycle 4  10400       125747488.
##  6 cycle 5  10400        91407833.
##  7 cycle 6  10400        71626014.
##  8 cycle 7  10400        33137201.
##  9 cycle 8  10400         6634434.
## 10 cycle 9  10400          319484.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            8248.
## 
## [[8]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220636028.
##  3 cycle 2  10400       137833435.
##  4 cycle 3  10400       113702545.
##  5 cycle 4  10400       125183169.
##  6 cycle 5  10400        91198830.
##  7 cycle 6  10400        72129123.
##  8 cycle 7  10400        33072990.
##  9 cycle 8  10400         6502053.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[9]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       137298914.
##  4 cycle 3  10400       113600819.
##  5 cycle 4  10400       125080307.
##  6 cycle 5  10400        91257185.
##  7 cycle 6  10400        71843318.
##  8 cycle 7  10400        32660143.
##  9 cycle 8  10400         6219449.
## 10 cycle 9  10400          238651.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            4124.
## 
## [[10]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221411398.
##  3 cycle 2  10400       138259029.
##  4 cycle 3  10400       115461029.
##  5 cycle 4  10400       126138419.
##  6 cycle 5  10400        92076668.
##  7 cycle 6  10400        73253976.
##  8 cycle 7  10400        34026166.
##  9 cycle 8  10400         6509794.
## 10 cycle 9  10400          223254.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           33503.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            8248.
## 
## [[11]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       137976038.
##  4 cycle 3  10400       115606067.
##  5 cycle 4  10400       125660889.
##  6 cycle 5  10400        90737364.
##  7 cycle 6  10400        71743144.
##  8 cycle 7  10400        32846519.
##  9 cycle 8  10400         6288855.
## 10 cycle 9  10400          238651.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400           12372.
## 
## [[12]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       137084062.
##  4 cycle 3  10400       114418823.
##  5 cycle 4  10400       126251603.
##  6 cycle 5  10400        91733304.
##  7 cycle 6  10400        72472465.
##  8 cycle 7  10400        33465786.
##  9 cycle 8  10400         6401781.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[13]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       137777470.
##  4 cycle 3  10400       114380971.
##  5 cycle 4  10400       125995975.
##  6 cycle 5  10400        90911954.
##  7 cycle 6  10400        71581503.
##  8 cycle 7  10400        32866880.
##  9 cycle 8  10400         6664257.
## 10 cycle 9  10400          269444.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           33503.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[14]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220463723.
##  3 cycle 2  10400       136768820.
##  4 cycle 3  10400       114234476.
##  5 cycle 4  10400       126033766.
##  6 cycle 5  10400        91134202.
##  7 cycle 6  10400        73051940.
##  8 cycle 7  10400        33170718.
##  9 cycle 8  10400         6845916.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[15]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220980637.
##  3 cycle 2  10400       137722294.
##  4 cycle 3  10400       115168040.
##  5 cycle 4  10400       126891904.
##  6 cycle 5  10400        92014364.
##  7 cycle 6  10400        72273623.
##  8 cycle 7  10400        33299457.
##  9 cycle 8  10400         6647762.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           10312.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           26657.
## 15 cycle 14 10400           12372.
## 
## [[16]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       137762293.
##  4 cycle 3  10400       114687609.
##  5 cycle 4  10400       125221951.
##  6 cycle 5  10400        91119557.
##  7 cycle 6  10400        71875173.
##  8 cycle 7  10400        33701655.
##  9 cycle 8  10400         6799095.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[17]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       137837545.
##  4 cycle 3  10400       114416820.
##  5 cycle 4  10400       124999754.
##  6 cycle 5  10400        90996341.
##  7 cycle 6  10400        72556603.
##  8 cycle 7  10400        33589205.
##  9 cycle 8  10400         7023201.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           22214.
## 15 cycle 14 10400            4124.
## 
## [[18]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       136983989.
##  4 cycle 3  10400       113878340.
##  5 cycle 4  10400       125928229.
##  6 cycle 5  10400        91313440.
##  7 cycle 6  10400        72024649.
##  8 cycle 7  10400        33427570.
##  9 cycle 8  10400         6554661.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            8248.
## 
## [[19]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220722180.
##  3 cycle 2  10400       137512342.
##  4 cycle 3  10400       113132034.
##  5 cycle 4  10400       124901947.
##  6 cycle 5  10400        90550912.
##  7 cycle 6  10400        72298260.
##  8 cycle 7  10400        33214258.
##  9 cycle 8  10400         6517098.
## 10 cycle 9  10400          234802.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            4124.
## 
## [[20]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       137758183.
##  4 cycle 3  10400       114066509.
##  5 cycle 4  10400       125419459.
##  6 cycle 5  10400        90900558.
##  7 cycle 6  10400        72318042.
##  8 cycle 7  10400        33142213.
##  9 cycle 8  10400         6333115.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[21]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220743718.
##  3 cycle 2  10400       136250583.
##  4 cycle 3  10400       113973880.
##  5 cycle 4  10400       126164013.
##  6 cycle 5  10400        91308092.
##  7 cycle 6  10400        73499018.
##  8 cycle 7  10400        33995156.
##  9 cycle 8  10400         6870891.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[22]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       137151095.
##  4 cycle 3  10400       115265944.
##  5 cycle 4  10400       126252192.
##  6 cycle 5  10400        91239282.
##  7 cycle 6  10400        71977036.
##  8 cycle 7  10400        33584816.
##  9 cycle 8  10400         6602525.
## 10 cycle 9  10400          188611.
## 11 cycle 10 10400           77763.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            4124.
## 
## [[23]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       136988099.
##  4 cycle 3  10400       114217734.
##  5 cycle 4  10400       125510019.
##  6 cycle 5  10400        90314010.
##  7 cycle 6  10400        71501296.
##  8 cycle 7  10400        33380899.
##  9 cycle 8  10400         6654496.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[24]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       137145561.
##  4 cycle 3  10400       114249216.
##  5 cycle 4  10400       125594428.
##  6 cycle 5  10400        91032612.
##  7 cycle 6  10400        72207825.
##  8 cycle 7  10400        33043544.
##  9 cycle 8  10400         6469840.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[25]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       136545431.
##  4 cycle 3  10400       113880160.
##  5 cycle 4  10400       125236738.
##  6 cycle 5  10400        90121052.
##  7 cycle 6  10400        71620300.
##  8 cycle 7  10400        33020051.
##  9 cycle 8  10400         6222579.
## 10 cycle 9  10400          204008.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           77340.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[26]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       137197734.
##  4 cycle 3  10400       113867240.
##  5 cycle 4  10400       126181476.
##  6 cycle 5  10400        91503608.
##  7 cycle 6  10400        73348344.
##  8 cycle 7  10400        34070959.
##  9 cycle 8  10400         6904651.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400            8248.
## 
## [[27]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       136534681.
##  4 cycle 3  10400       113556051.
##  5 cycle 4  10400       125334840.
##  6 cycle 5  10400        90322849.
##  7 cycle 6  10400        72155634.
##  8 cycle 7  10400        33015667.
##  9 cycle 8  10400         6622587.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[28]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       137780473.
##  4 cycle 3  10400       114693797.
##  5 cycle 4  10400       126140399.
##  6 cycle 5  10400        91765384.
##  7 cycle 6  10400        72296109.
##  8 cycle 7  10400        33230860.
##  9 cycle 8  10400         6222949.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            8248.
## 
## [[29]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       136292795.
##  4 cycle 3  10400       114048310.
##  5 cycle 4  10400       125869204.
##  6 cycle 5  10400        91762593.
##  7 cycle 6  10400        73133652.
##  8 cycle 7  10400        33154430.
##  9 cycle 8  10400         6480208.
## 10 cycle 9  10400          227103.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[30]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221217556.
##  3 cycle 2  10400       137814148.
##  4 cycle 3  10400       114215915.
##  5 cycle 4  10400       125371641.
##  6 cycle 5  10400        90665289.
##  7 cycle 6  10400        71672215.
##  8 cycle 7  10400        32901959.
##  9 cycle 8  10400         6595354.
## 10 cycle 9  10400          230952.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           38289.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[31]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220528337.
##  3 cycle 2  10400       136694357.
##  4 cycle 3  10400       113501456.
##  5 cycle 4  10400       124983491.
##  6 cycle 5  10400        90617630.
##  7 cycle 6  10400        72351587.
##  8 cycle 7  10400        33248399.
##  9 cycle 8  10400         6454692.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400           12372.
## 
## [[32]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       137537797.
##  4 cycle 3  10400       114719820.
##  5 cycle 4  10400       124836876.
##  6 cycle 5  10400        90855457.
##  7 cycle 6  10400        72364796.
##  8 cycle 7  10400        33502123.
##  9 cycle 8  10400         6382999.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400            8248.
## 
## [[33]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221260632.
##  3 cycle 2  10400       137472978.
##  4 cycle 3  10400       114564227.
##  5 cycle 4  10400       126896770.
##  6 cycle 5  10400        92371449.
##  7 cycle 6  10400        73318915.
##  8 cycle 7  10400        34080045.
##  9 cycle 8  10400         7039356.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[34]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220851408.
##  3 cycle 2  10400       136762497.
##  4 cycle 3  10400       113286719.
##  5 cycle 4  10400       125650483.
##  6 cycle 5  10400        91708887.
##  7 cycle 6  10400        73155585.
##  8 cycle 7  10400        33563516.
##  9 cycle 8  10400         6544900.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[35]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       138147571.
##  4 cycle 3  10400       115108895.
##  5 cycle 4  10400       126072252.
##  6 cycle 5  10400        91657755.
##  7 cycle 6  10400        71959219.
##  8 cycle 7  10400        32815510.
##  9 cycle 8  10400         6425950.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           77340.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[36]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       137134021.
##  4 cycle 3  10400       114353129.
##  5 cycle 4  10400       125381268.
##  6 cycle 5  10400        90469073.
##  7 cycle 6  10400        72000842.
##  8 cycle 7  10400        32886613.
##  9 cycle 8  10400         6476101.
## 10 cycle 9  10400          227103.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[37]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       137587439.
##  4 cycle 3  10400       114523098.
##  5 cycle 4  10400       126198139.
##  6 cycle 5  10400        91919514.
##  7 cycle 6  10400        73337377.
##  8 cycle 7  10400        33855139.
##  9 cycle 8  10400         6315444.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400            8248.
## 
## [[38]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       137704903.
##  4 cycle 3  10400       113645403.
##  5 cycle 4  10400       125122859.
##  6 cycle 5  10400        90906839.
##  7 cycle 6  10400        72008614.
##  8 cycle 7  10400        32911045.
##  9 cycle 8  10400         6500840.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400           26657.
## 15 cycle 14 10400            4124.
## 
## [[39]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       137672335.
##  4 cycle 3  10400       113975516.
##  5 cycle 4  10400       124863861.
##  6 cycle 5  10400        90752700.
##  7 cycle 6  10400        71830385.
##  8 cycle 7  10400        33522170.
##  9 cycle 8  10400         6476034.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400               0 
## 
## [[40]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       138298710.
##  4 cycle 3  10400       114342390.
##  5 cycle 4  10400       125579155.
##  6 cycle 5  10400        91701223.
##  7 cycle 6  10400        72549108.
##  8 cycle 7  10400        33135324.
##  9 cycle 8  10400         6319181.
## 10 cycle 9  10400          219405.
## 11 cycle 10 10400           61099.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400           12372.
## 
## [[41]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       137210381.
##  4 cycle 3  10400       113507098.
##  5 cycle 4  10400       126260429.
##  6 cycle 5  10400        91288565.
##  7 cycle 6  10400        73103763.
##  8 cycle 7  10400        32935165.
##  9 cycle 8  10400         6535746.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            8248.
## 
## [[42]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       137409265.
##  4 cycle 3  10400       113633757.
##  5 cycle 4  10400       125971181.
##  6 cycle 5  10400        92171061.
##  7 cycle 6  10400        73618391.
##  8 cycle 7  10400        33903691.
##  9 cycle 8  10400         6783814.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[43]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220399109.
##  3 cycle 2  10400       136462432.
##  4 cycle 3  10400       113633212.
##  5 cycle 4  10400       125129010.
##  6 cycle 5  10400        90857547.
##  7 cycle 6  10400        71740902.
##  8 cycle 7  10400        32797653.
##  9 cycle 8  10400         6472970.
## 10 cycle 9  10400          215556.
## 11 cycle 10 10400           72208.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[44]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221454474.
##  3 cycle 2  10400       138100460.
##  4 cycle 3  10400       114255586.
##  5 cycle 4  10400       125303789.
##  6 cycle 5  10400        90635533.
##  7 cycle 6  10400        71839478.
##  8 cycle 7  10400        32831796.
##  9 cycle 8  10400         6307636.
## 10 cycle 9  10400          257897.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           22214.
## 15 cycle 14 10400            8248.
## 
## [[45]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       137725615.
##  4 cycle 3  10400       114234660.
##  5 cycle 4  10400       125785090.
##  6 cycle 5  10400        91413639.
##  7 cycle 6  10400        73028901.
##  8 cycle 7  10400        33722013.
##  9 cycle 8  10400         6605419.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[46]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       138068998.
##  4 cycle 3  10400       115087968.
##  5 cycle 4  10400       125261532.
##  6 cycle 5  10400        90934505.
##  7 cycle 6  10400        72045815.
##  8 cycle 7  10400        32804232.
##  9 cycle 8  10400         6522685.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400           12372.
## 
## [[47]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220679104.
##  3 cycle 2  10400       137586332.
##  4 cycle 3  10400       114655945.
##  5 cycle 4  10400       126315683.
##  6 cycle 5  10400        91758869.
##  7 cycle 6  10400        73300454.
##  8 cycle 7  10400        33822562.
##  9 cycle 8  10400         6560181.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          133307.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[48]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       137624117.
##  4 cycle 3  10400       114075062.
##  5 cycle 4  10400       125788755.
##  6 cycle 5  10400        91407133.
##  7 cycle 6  10400        72268371.
##  8 cycle 7  10400        33431330.
##  9 cycle 8  10400         6605352.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          211070.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           33503.
## 14 cycle 13 10400           26657.
## 15 cycle 14 10400               0 
## 
## [[49]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       137992004.
##  4 cycle 3  10400       114278516.
##  5 cycle 4  10400       125387608.
##  6 cycle 5  10400        91469670.
##  7 cycle 6  10400        72407434.
##  8 cycle 7  10400        33718256.
##  9 cycle 8  10400         6373239.
## 10 cycle 9  10400          215556.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            8248.
## 
## [[50]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       137142558.
##  4 cycle 3  10400       113787714.
##  5 cycle 4  10400       125710097.
##  6 cycle 5  10400        91338548.
##  7 cycle 6  10400        72712070.
##  8 cycle 7  10400        33587637.
##  9 cycle 8  10400         6632888.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            8248.
## 
## [[51]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       138294600.
##  4 cycle 3  10400       114211730.
##  5 cycle 4  10400       125438502.
##  6 cycle 5  10400        90549046.
##  7 cycle 6  10400        72579488.
##  8 cycle 7  10400        33871427.
##  9 cycle 8  10400         6610199.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[52]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221174479.
##  3 cycle 2  10400       137655261.
##  4 cycle 3  10400       113438856.
##  5 cycle 4  10400       125818521.
##  6 cycle 5  10400        91554981.
##  7 cycle 6  10400        72938005.
##  8 cycle 7  10400        33740494.
##  9 cycle 8  10400         6526555.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[53]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220765256.
##  3 cycle 2  10400       136899248.
##  4 cycle 3  10400       113971694.
##  5 cycle 4  10400       124913554.
##  6 cycle 5  10400        90721320.
##  7 cycle 6  10400        72658835.
##  8 cycle 7  10400        32929215.
##  9 cycle 8  10400         6368931.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           25780.
## 13 cycle 12 10400           33503.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[54]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220420647.
##  3 cycle 2  10400       137033314.
##  4 cycle 3  10400       114449031.
##  5 cycle 4  10400       125155489.
##  6 cycle 5  10400        90914978.
##  7 cycle 6  10400        72696220.
##  8 cycle 7  10400        33441042.
##  9 cycle 8  10400         6718448.
## 10 cycle 9  10400          331032.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[55]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       137199313.
##  4 cycle 3  10400       113698540.
##  5 cycle 4  10400       124913849.
##  6 cycle 5  10400        90737598.
##  7 cycle 6  10400        72097913.
##  8 cycle 7  10400        33159443.
##  9 cycle 8  10400         6439951.
## 10 cycle 9  10400          257897.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[56]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       137397408.
##  4 cycle 3  10400       114286705.
##  5 cycle 4  10400       125642836.
##  6 cycle 5  10400        91501059.
##  7 cycle 6  10400        72402550.
##  8 cycle 7  10400        33296328.
##  9 cycle 8  10400         6908995.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          183298.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[57]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220872946.
##  3 cycle 2  10400       137237415.
##  4 cycle 3  10400       114796798.
##  5 cycle 4  10400       125991214.
##  6 cycle 5  10400        91634729.
##  7 cycle 6  10400        72294236.
##  8 cycle 7  10400        33157249.
##  9 cycle 8  10400         6818720.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400           88872.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[58]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221368322.
##  3 cycle 2  10400       136977666.
##  4 cycle 3  10400       113517836.
##  5 cycle 4  10400       125584717.
##  6 cycle 5  10400        91515228.
##  7 cycle 6  10400        73188668.
##  8 cycle 7  10400        34238854.
##  9 cycle 8  10400         6829828.
## 10 cycle 9  10400          338730.
## 11 cycle 10 10400           99981.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400            4124.
## 
## [[59]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       137218128.
##  4 cycle 3  10400       114531105.
##  5 cycle 4  10400       124922381.
##  6 cycle 5  10400        90296583.
##  7 cycle 6  10400        71138080.
##  8 cycle 7  10400        32877530.
##  9 cycle 8  10400         6777117.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[60]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       137362627.
##  4 cycle 3  10400       113845947.
##  5 cycle 4  10400       125176323.
##  6 cycle 5  10400        90949149.
##  7 cycle 6  10400        71882207.
##  8 cycle 7  10400        33254352.
##  9 cycle 8  10400         6221165.
## 10 cycle 9  10400          261746.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[61]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       138025679.
##  4 cycle 3  10400       115313260.
##  5 cycle 4  10400       125541954.
##  6 cycle 5  10400        91813285.
##  7 cycle 6  10400        72852424.
##  8 cycle 7  10400        33382782.
##  9 cycle 8  10400         6895867.
## 10 cycle 9  10400          307937.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           38289.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            8248.
## 
## [[62]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221239094.
##  3 cycle 2  10400       136630962.
##  4 cycle 3  10400       114235205.
##  5 cycle 4  10400       126851438.
##  6 cycle 5  10400        92389585.
##  7 cycle 6  10400        72840074.
##  8 cycle 7  10400        33509013.
##  9 cycle 8  10400         6457519.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[63]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221540627.
##  3 cycle 2  10400       137728145.
##  4 cycle 3  10400       114279427.
##  5 cycle 4  10400       126331756.
##  6 cycle 5  10400        91875804.
##  7 cycle 6  10400        73578948.
##  8 cycle 7  10400        34296802.
##  9 cycle 8  10400         7057531.
## 10 cycle 9  10400          327183.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[64]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       137155522.
##  4 cycle 3  10400       114478514.
##  5 cycle 4  10400       126075033.
##  6 cycle 5  10400        91787468.
##  7 cycle 6  10400        72552395.
##  8 cycle 7  10400        33620212.
##  9 cycle 8  10400         6630394.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            8248.
## 
## [[65]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       138269779.
##  4 cycle 3  10400       114359316.
##  5 cycle 4  10400       126313998.
##  6 cycle 5  10400        91512204.
##  7 cycle 6  10400        74271161.
##  8 cycle 7  10400        33963205.
##  9 cycle 8  10400         6741100.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           36092.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.
## 
## [[66]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       136283151.
##  4 cycle 3  10400       112560070.
##  5 cycle 4  10400       125816140.
##  6 cycle 5  10400        91472918.
##  7 cycle 6  10400        72470222.
##  8 cycle 7  10400        33856078.
##  9 cycle 8  10400         6778967.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           82496.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[67]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       136692144.
##  4 cycle 3  10400       112665436.
##  5 cycle 4  10400       125263218.
##  6 cycle 5  10400        90558584.
##  7 cycle 6  10400        71880149.
##  8 cycle 7  10400        32982463.
##  9 cycle 8  10400         6434800.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400               0 
## 
## [[68]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       136957744.
##  4 cycle 3  10400       113763327.
##  5 cycle 4  10400       124846398.
##  6 cycle 5  10400        90514650.
##  7 cycle 6  10400        71756353.
##  8 cycle 7  10400        33015354.
##  9 cycle 8  10400         6469033.
## 10 cycle 9  10400          327183.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            4124.
## 
## [[69]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221088327.
##  3 cycle 2  10400       137455587.
##  4 cycle 3  10400       114218280.
##  5 cycle 4  10400       125917612.
##  6 cycle 5  10400        91895564.
##  7 cycle 6  10400        73151837.
##  8 cycle 7  10400        33437595.
##  9 cycle 8  10400         6680715.
## 10 cycle 9  10400          246349.
## 11 cycle 10 10400           83317.
## 12 cycle 11 10400           15468.
## 13 cycle 12 10400           28717.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400               0 
## 
## [[70]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220700642.
##  3 cycle 2  10400       135983876.
##  4 cycle 3  10400       113552228.
##  5 cycle 4  10400       125111547.
##  6 cycle 5  10400        91427359.
##  7 cycle 6  10400        71963550.
##  8 cycle 7  10400        33500241.
##  9 cycle 8  10400         6491382.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           72184.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[71]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       137582223.
##  4 cycle 3  10400       114327834.
##  5 cycle 4  10400       126333147.
##  6 cycle 5  10400        90663889.
##  7 cycle 6  10400        72771203.
##  8 cycle 7  10400        33948485.
##  9 cycle 8  10400         6931343.
## 10 cycle 9  10400          311786.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           46404.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[72]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221389860.
##  3 cycle 2  10400       136913636.
##  4 cycle 3  10400       114178063.
##  5 cycle 4  10400       125825957.
##  6 cycle 5  10400        91687269.
##  7 cycle 6  10400        72892081.
##  8 cycle 7  10400        33806274.
##  9 cycle 8  10400         6610569.
## 10 cycle 9  10400          273294.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[73]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221131403.
##  3 cycle 2  10400       136361724.
##  4 cycle 3  10400       113902360.
##  5 cycle 4  10400       125184855.
##  6 cycle 5  10400        90826633.
##  7 cycle 6  10400        72315217.
##  8 cycle 7  10400        33396563.
##  9 cycle 8  10400         6609762.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[74]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       137657475.
##  4 cycle 3  10400       114490159.
##  5 cycle 4  10400       125213524.
##  6 cycle 5  10400        91306234.
##  7 cycle 6  10400        72658466.
##  8 cycle 7  10400        33908704.
##  9 cycle 8  10400         6718448.
## 10 cycle 9  10400          304087.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400           12372.
## 
## [[75]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220937561.
##  3 cycle 2  10400       137698580.
##  4 cycle 3  10400       114967496.
##  5 cycle 4  10400       125770702.
##  6 cycle 5  10400        91092349.
##  7 cycle 6  10400        72598503.
##  8 cycle 7  10400        33190139.
##  9 cycle 8  10400         6620130.
## 10 cycle 9  10400          277143.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[76]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       137295911.
##  4 cycle 3  10400       113608280.
##  5 cycle 4  10400       125252706.
##  6 cycle 5  10400        90720387.
##  7 cycle 6  10400        71770330.
##  8 cycle 7  10400        33016604.
##  9 cycle 8  10400         6526555.
## 10 cycle 9  10400          227103.
## 11 cycle 10 10400          111090.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[77]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       136977348.
##  4 cycle 3  10400       112514756.
##  5 cycle 4  10400       125406462.
##  6 cycle 5  10400        91294838.
##  7 cycle 6  10400        73595536.
##  8 cycle 7  10400        34004240.
##  9 cycle 8  10400         6957732.
## 10 cycle 9  10400          315635.
## 11 cycle 10 10400          155525.
## 12 cycle 11 10400           72184.
## 13 cycle 12 10400           33503.
## 14 cycle 13 10400           26657.
## 15 cycle 14 10400           12372.
## 
## [[78]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221196018.
##  3 cycle 2  10400       137795650.
##  4 cycle 3  10400       114220828.
##  5 cycle 4  10400       125336820.
##  6 cycle 5  10400        90456061.
##  7 cycle 6  10400        72121628.
##  8 cycle 7  10400        32875649.
##  9 cycle 8  10400         6208341.
## 10 cycle 9  10400          250198.
## 11 cycle 10 10400          161080.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           43075.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[79]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221109865.
##  3 cycle 2  10400       137265556.
##  4 cycle 3  10400       113291087.
##  5 cycle 4  10400       125438102.
##  6 cycle 5  10400        91479899.
##  7 cycle 6  10400        73150424.
##  8 cycle 7  10400        33571661.
##  9 cycle 8  10400         6460819.
## 10 cycle 9  10400          207857.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[80]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       137461121.
##  4 cycle 3  10400       113710190.
##  5 cycle 4  10400       126028416.
##  6 cycle 5  10400        91301586.
##  7 cycle 6  10400        72446692.
##  8 cycle 7  10400        33490219.
##  9 cycle 8  10400         6812290.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          144416.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[81]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220894484.
##  3 cycle 2  10400       137509656.
##  4 cycle 3  10400       114620641.
##  5 cycle 4  10400       125905015.
##  6 cycle 5  10400        90618564.
##  7 cycle 6  10400        72667374.
##  8 cycle 7  10400        33552554.
##  9 cycle 8  10400         7023031.
## 10 cycle 9  10400          350278.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400               0 
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            8248.
## 
## [[82]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       137226665.
##  4 cycle 3  10400       114812994.
##  5 cycle 4  10400       126502069.
##  6 cycle 5  10400        91191391.
##  7 cycle 6  10400        72426172.
##  8 cycle 7  10400        33538458.
##  9 cycle 8  10400         6702797.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           43075.
## 14 cycle 13 10400           17771.
## 15 cycle 14 10400            4124.
## 
## [[83]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       137127381.
##  4 cycle 3  10400       113886347.
##  5 cycle 4  10400       125314406.
##  6 cycle 5  10400        91148847.
##  7 cycle 6  10400        72802966.
##  8 cycle 7  10400        33598599.
##  9 cycle 8  10400         6563548.
## 10 cycle 9  10400          207857.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[84]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       137619690.
##  4 cycle 3  10400       114871773.
##  5 cycle 4  10400       125465571.
##  6 cycle 5  10400        90376322.
##  7 cycle 6  10400        71897751.
##  8 cycle 7  10400        33097735.
##  9 cycle 8  10400         6835045.
## 10 cycle 9  10400          265595.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            4124.
## 
## [[85]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       138199743.
##  4 cycle 3  10400       114019922.
##  5 cycle 4  10400       126341973.
##  6 cycle 5  10400        91647283.
##  7 cycle 6  10400        72617610.
##  8 cycle 7  10400        33554121.
##  9 cycle 8  10400         6793441.
## 10 cycle 9  10400          327183.
## 11 cycle 10 10400          188852.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[86]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220614490.
##  3 cycle 2  10400       136939246.
##  4 cycle 3  10400       113528754.
##  5 cycle 4  10400       125319567.
##  6 cycle 5  10400        90615997.
##  7 cycle 6  10400        72511170.
##  8 cycle 7  10400        33709798.
##  9 cycle 8  10400         6421539.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[87]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221045251.
##  3 cycle 2  10400       137500802.
##  4 cycle 3  10400       113453050.
##  5 cycle 4  10400       125532727.
##  6 cycle 5  10400        91385049.
##  7 cycle 6  10400        71932617.
##  8 cycle 7  10400        33359914.
##  9 cycle 8  10400         6513901.
## 10 cycle 9  10400          284841.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           30936.
## 13 cycle 12 10400            4786.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            4124.
## 
## [[88]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221023713.
##  3 cycle 2  10400       138291280.
##  4 cycle 3  10400       114346213.
##  5 cycle 4  10400       125172257.
##  6 cycle 5  10400        90615773.
##  7 cycle 6  10400        71435129.
##  8 cycle 7  10400        33114963.
##  9 cycle 8  10400         6571089.
## 10 cycle 9  10400          280992.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[89]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220506799.
##  3 cycle 2  10400       137595976.
##  4 cycle 3  10400       114539840.
##  5 cycle 4  10400       125809294.
##  6 cycle 5  10400        91470827.
##  7 cycle 6  10400        72258909.
##  8 cycle 7  10400        33850127.
##  9 cycle 8  10400         6661193.
## 10 cycle 9  10400          323333.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400               0 
## 
## [[90]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220959099.
##  3 cycle 2  10400       136930710.
##  4 cycle 3  10400       113282351.
##  5 cycle 4  10400       125429570.
##  6 cycle 5  10400        90628327.
##  7 cycle 6  10400        71369913.
##  8 cycle 7  10400        32697418.
##  9 cycle 8  10400         6656650.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          138862.
## 12 cycle 11 10400           20624.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[91]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220808332.
##  3 cycle 2  10400       137364051.
##  4 cycle 3  10400       114400990.
##  5 cycle 4  10400       126502470.
##  6 cycle 5  10400        91963915.
##  7 cycle 6  10400        72661476.
##  8 cycle 7  10400        33590454.
##  9 cycle 8  10400         6499796.
## 10 cycle 9  10400          288691.
## 11 cycle 10 10400          149971.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           38289.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[92]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221002175.
##  3 cycle 2  10400       137242949.
##  4 cycle 3  10400       113201189.
##  5 cycle 4  10400       125489291.
##  6 cycle 5  10400        90689230.
##  7 cycle 6  10400        72936500.
##  8 cycle 7  10400        33920606.
##  9 cycle 8  10400         6746014.
## 10 cycle 9  10400          296389.
## 11 cycle 10 10400          122198.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400            4124.
## 
## [[93]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221066789.
##  3 cycle 2  10400       138209387.
##  4 cycle 3  10400       114498349.
##  5 cycle 4  10400       126823758.
##  6 cycle 5  10400        92399357.
##  7 cycle 6  10400        73423390.
##  8 cycle 7  10400        33885211.
##  9 cycle 8  10400         6989708.
## 10 cycle 9  10400          377222.
## 11 cycle 10 10400          222179.
## 12 cycle 11 10400           77340.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[94]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220829870.
##  3 cycle 2  10400       137684509.
##  4 cycle 3  10400       114024652.
##  5 cycle 4  10400       124750677.
##  6 cycle 5  10400        90560909.
##  7 cycle 6  10400        71825040.
##  8 cycle 7  10400        32988729.
##  9 cycle 8  10400         6421473.
## 10 cycle 9  10400          292540.
## 11 cycle 10 10400          127753.
## 12 cycle 11 10400           61872.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           13328.
## 15 cycle 14 10400            8248.
## 
## [[95]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220657566.
##  3 cycle 2  10400       137062562.
##  4 cycle 3  10400       113387900.
##  5 cycle 4  10400       125301514.
##  6 cycle 5  10400        91370628.
##  7 cycle 6  10400        72752741.
##  8 cycle 7  10400        33163513.
##  9 cycle 8  10400         6557658.
## 10 cycle 9  10400          327183.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[96]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221152941.
##  3 cycle 2  10400       137874540.
##  4 cycle 3  10400       114510178.
##  5 cycle 4  10400       126278587.
##  6 cycle 5  10400        91750264.
##  7 cycle 6  10400        73313202.
##  8 cycle 7  10400        33751769.
##  9 cycle 8  10400         6632918.
## 10 cycle 9  10400          238651.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           56716.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400               0 
## 15 cycle 14 10400            8248.
## 
## [[97]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220916023.
##  3 cycle 2  10400       137069202.
##  4 cycle 3  10400       114392800.
##  5 cycle 4  10400       125990224.
##  6 cycle 5  10400        91882085.
##  7 cycle 6  10400        73382350.
##  8 cycle 7  10400        34100403.
##  9 cycle 8  10400         6472970.
## 10 cycle 9  10400          200159.
## 11 cycle 10 10400          105535.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400           19144.
## 14 cycle 13 10400           26657.
## 15 cycle 14 10400            4124.
## 
## [[98]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220571413.
##  3 cycle 2  10400       137588229.
##  4 cycle 3  10400       114393891.
##  5 cycle 4  10400       125414488.
##  6 cycle 5  10400        90489775.
##  7 cycle 6  10400        71823535.
##  8 cycle 7  10400        33298207.
##  9 cycle 8  10400         6652409.
## 10 cycle 9  10400          300238.
## 11 cycle 10 10400           94426.
## 12 cycle 11 10400           51560.
## 13 cycle 12 10400            9572.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400               0 
## 
## [[99]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       221325246.
##  3 cycle 2  10400       138070105.
##  4 cycle 3  10400       114296532.
##  5 cycle 4  10400       125524301.
##  6 cycle 5  10400        90848951.
##  7 cycle 6  10400        72026892.
##  8 cycle 7  10400        33063277.
##  9 cycle 8  10400         6660823.
## 10 cycle 9  10400          254048.
## 11 cycle 10 10400          166634.
## 12 cycle 11 10400           41248.
## 13 cycle 12 10400           23931.
## 14 cycle 13 10400            4443.
## 15 cycle 14 10400               0 
## 
## [[100]]
## # A tibble: 15 × 3
##    cycle        n discounted_costs
##    <fct>    <int>            <dbl>
##  1 cycle 0  10400       261295475.
##  2 cycle 1  10400       220786794.
##  3 cycle 2  10400       137749329.
##  4 cycle 3  10400       114643570.
##  5 cycle 4  10400       125908385.
##  6 cycle 5  10400        90973324.
##  7 cycle 6  10400        72756673.
##  8 cycle 7  10400        33700714.
##  9 cycle 8  10400         6646149.
## 10 cycle 9  10400          242500.
## 11 cycle 10 10400          116644.
## 12 cycle 11 10400           67028.
## 13 cycle 12 10400           14358.
## 14 cycle 13 10400            8886.
## 15 cycle 14 10400            4124.

The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:

#Males
tot_discounted_costs_m_altB <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m_altB[[i]]$discounted_costs) 
tot_discounted_costs_m_altB[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m_altB)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1452038039
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1449790595
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1450330590
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1449879060
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1447173827
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1454921000
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1454358094
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1447716117
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1445874599
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1451511628
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1455014293
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1452981593
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1447801863
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1447957520
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1447144095
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1449978872
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1446432574
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1442486365
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1451296958
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1445226183
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1445619814
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1448950948
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1450222360
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1449572171
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1450594866
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1454266868
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1442155925
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1445650410
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1449324140
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1450243806
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1447165852
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1445168766
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1449328712
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1447057199
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1448233401
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1445961556
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1444835586
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1450128274
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1450200481
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1447821582
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1448547838
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1447767577
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1452629553
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1445954840
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1443568998
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1447140487
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1448832776
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1446005553
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1450330752
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1444911515
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1448213133
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1452058524
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1446803381
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1445499982
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1445907542
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1454132714
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1450852663
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1453790044
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1438275737
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1441519852
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1448480290
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1447823061
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1445957303
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1442142086
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1448567612
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1442513982
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1445089172
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1446177704
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1446170829
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1449191836
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1442153365
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1450991309
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1447677302
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1449980518
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1447874672
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1446910882
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1450863376
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1449312128
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1449286968
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1444551519
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1445754168
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1452105015
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1447126668
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1448196401
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1447301551
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1447775873
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1445709121
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1447470515
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1446596643
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1452800030
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1450237203
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1449520484
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1448245232
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1446609231
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1448968246
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1444942994
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1448845283
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1449881850
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1452795631
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1451200319
#Females
tot_discounted_costs_f_altB <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f_altB[[i]]$discounted_costs) 
tot_discounted_costs_f_altB[[i]] <- list(
  "tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f_altB)
## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1070548798
## 
## 
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1064373992
## 
## 
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1066523956
## 
## 
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1061865412
## 
## 
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1062778628
## 
## 
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1064508172
## 
## 
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1064316293
## 
## 
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1062013017
## 
## 
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1060717042
## 
## 
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1068836678
## 
## 
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1063549901
## 
## 
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1064396530
## 
## 
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1063139993
## 
## 
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1063521011
## 
## 
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1066697263
## 
## 
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1063934105
## 
## 
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1063872529
## 
## 
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1063093149
## 
## 
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1060586812
## 
## 
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1062851934
## 
## 
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1064522863
## 
## 
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1064391319
## 
## 
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1061280567
## 
## 
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1062338475
## 
## 
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1059315816
## 
## 
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1065807304
## 
## 
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1060084955
## 
## 
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1065135531
## 
## 
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1063135152
## 
## 
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1062146773
## 
## 
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1060169406
## 
## 
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1063200562
## 
## 
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1068726653
## 
## 
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1063308680
## 
## 
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1064908838
## 
## 
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1061534220
## 
## 
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1066459415
## 
## 
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1061667971
## 
## 
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1061824201
## 
## 
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1064626518
## 
## 
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1063769225
## 
## 
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1066104022
## 
## 
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1059160158
## 
## 
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1062516598
## 
## 
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1065126219
## 
## 
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1063223800
## 
## 
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1066464423
## 
## 
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1064194166
## 
## 
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1064130407
## 
## 
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1063568957
## 
## 
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1064387273
## 
## 
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1064524639
## 
## 
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1060991711
## 
## 
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1062607637
## 
## 
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1060888536
## 
## 
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1064079355
## 
## 
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1064521815
## 
## 
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1065042749
## 
## 
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1060167825
## 
## 
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1061116700
## 
## 
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1066537621
## 
## 
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1065990226
## 
## 
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1068508041
## 
## 
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1064964344
## 
## 
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1068604912
## 
## 
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1062136890
## 
## 
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1059091260
## 
## 
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1059889045
## 
## 
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1065532613
## 
## 
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1060535757
## 
## 
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1065523179
## 
## 
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1065084062
## 
## 
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1061492196
## 
## 
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1064662076
## 
## 
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1064655221
## 
## 
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1060818450
## 
## 
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1063556749
## 
## 
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1062026425
## 
## 
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1063431148
## 
## 
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1063918896
## 
## 
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1064655736
## 
## 
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1065268176
## 
## 
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1063098087
## 
## 
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1062915065
## 
## 
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1066120387
## 
## 
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1061427895
## 
## 
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1062437899
## 
## 
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1062372372
## 
## 
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1064544697
## 
## 
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1059716147
## 
## 
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1065639504
## 
## 
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1063001402
## 
## 
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1069291409
## 
## 
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1060904220
## 
## 
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1062078130
## 
## 
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1066967487
## 
## 
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1065908711
## 
## 
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1061992105
## 
## 
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1063601905
## 
## 
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1064913954
#Averaging total costs across simulations
TDC_m_alternativeB <- mean(unlist(tot_discounted_costs_m_altB))
TDC_f_alternativeB <- mean(unlist(tot_discounted_costs_f_altB))
#Final result
TDC_alternativeB <- TDC_m_alternativeB + TDC_f_alternativeB
TDC_alternativeB
## [1] 2511860695

The total amount of money that can be saved thanks to early detection is:

total_savingsB <- TDC_baseline - TDC_alternativeB
total_savingsB
## [1] -387202101

The following is a useful graph to evaluate the trends of P, MPD, APD and D patients over the microsimulation time period:

prepare_plot_data <- function(df_m, scenario) {
  df_m %>%
    as_tibble() %>%
    pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
    count(cycle, state) %>%
    group_by(cycle) %>%
    mutate(percent = n / sum(n)) %>%
    ungroup() %>%
    mutate(scenario = scenario)
}


num_cols_m <- ncol(model_results_m[[50]])
num_cols_m_altB <- ncol(model_results_m_altB[[50]])

colnames(model_results_m[[50]]) <- paste("cycle", 0:(num_cols_m-1), sep = " ")
colnames(model_results_m_altB[[50]]) <- paste("cycle", 0:(num_cols_m_altB-1), sep = " ")


# Baseline
df_m.M <- model_results_m[[50]] %>% prepare_plot_data("Baseline")

# Alternative
df_m.M_altB <- model_results_m_altB[[50]] %>% prepare_plot_data("Alternative")

# Combining
combined_data_mB <- bind_rows(df_m.M, df_m.M_altB)

combined_data1B <- combined_data_mB %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% 
filter(cycle != "cycle 15")

# Plot 
summary_plot_maleB <- ggplot(combined_data1B %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
  geom_line() +
  labs(title = "Comparison of states across cycles and scenarios (Males)",
       x = "Cycle",
       y = "Percentage") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_maleB

The graph for females:

prepare_plot_data <- function(df_m, scenario) {
  df_m %>%
    as_tibble() %>%
    pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
    count(cycle, state) %>%
    group_by(cycle) %>%
    mutate(percent = n / sum(n)) %>%
    ungroup() %>%
    mutate(scenario = scenario)
}


num_cols_f <- ncol(model_results_f[[50]])
num_cols_f_altB <- ncol(model_results_f_altB[[50]])

colnames(model_results_f[[50]]) <- paste("cycle", 0:(num_cols_f-1), sep = " ")
colnames(model_results_f_altB[[50]]) <- paste("cycle", 0:(num_cols_f_altB-1), sep = " ")


# Baseline
df_m.M <- model_results_f[[50]] %>% prepare_plot_data("Baseline")

# Alternative
df_m.M_altB <- model_results_f_altB[[50]] %>% prepare_plot_data("Alternative")

# Combining
combined_data_fB <- bind_rows(df_m.M, df_m.M_altB)

combined_data2B <- combined_data_fB %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% 
filter(cycle != "cycle 15")

# Plot 
summary_plot_femaleB <- ggplot(combined_data2B %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
  geom_line() +
  labs(title = "Comparison of states across cycles and scenarios (Females)",
       x = "Cycle",
       y = "Percentage") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_femaleB

Losses are really prominent from a financial point of view, as indicated by the final result and by the graph comparing costs across scenarios:

However, if the point of view of patients is considered, the previous remarks represent a gain both in terms of life quality and life expectancy.

Let’s evaluate this gain:

process_model_result <- function(model_result) {
  df <- model_result %>% as_tibble()
  cycle_columns <- paste0("cycle ", 0:14)
  map(cycle_columns, ~ df %>% tabyl(!!sym(.x)))
}

# Males
percent_tables_m <- map(model_results_m[1:100], process_model_result)

# Females
percent_tables_f <- map(model_results_f[1:100], process_model_result)

# Aggregate results and compute the averages
aggregate_results <- function(percent_tables) {
  all_states <- c("P", "MPD", "APD", "D")
  cycle_columns <- paste0("cycle ", 0:14)
  
  aggregated <- map(cycle_columns, function(cycle) {
    state_sums <- map_dbl(all_states, function(state) {
      state_n_values <- map_dbl(percent_tables, ~ {
        tabyl_result <- .x[[which(cycle_columns == cycle)]]
        if (state %in% tabyl_result[[1]]) {
          return(tabyl_result$n[tabyl_result[[1]] == state])
        } else {
          return(0)
        }
      })
      mean(state_n_values)
    })
    tibble(state = all_states, mean_n = state_sums)
  })
  
  bind_rows(aggregated, .id = "cycle") %>%
    mutate(cycle = as.numeric(cycle) - 1) # Aggiustare i cicli da 0 a 14
}

# Aggregate for males
aggregated_m <- aggregate_results(percent_tables_m)

# Aggregate for females
aggregated_f <- aggregate_results(percent_tables_f)

aggregated_m
aggregated_f
#Same approach for the alternative scenario

percent_tables_m_altB <- map(model_results_m_altB[1:100], process_model_result)
percent_tables_f_altB <- map(model_results_f_altB[1:100], process_model_result)

# Aggregate for males
aggregated_m_altB <- aggregate_results(percent_tables_m_altB)

# Aggregate for females
aggregated_f_altB <- aggregate_results(percent_tables_f_altB)

aggregated_m_altB
aggregated_f_altB

With the new tables at hand it is possible to compute the 3 differences that indicate a gain for patients:

library(dplyr)

calculate_differences <- function(baseline, alternativeB) {
  baseline %>%
    inner_join(alternativeB, by = c("cycle", "state"), suffix = c("_baseline", "_altB")) %>%
    mutate(
      difference = case_when(
        state == "MPD" ~ mean_n_altB - mean_n_baseline,
        state == "APD" ~ mean_n_baseline - mean_n_altB,
        state == "D" ~ mean_n_baseline - mean_n_altB,
        TRUE ~ NA_real_
      )
    ) %>%
    select(cycle, state, difference) %>%
    filter(!is.na(difference))
}

differences_mB <- calculate_differences(aggregated_m, aggregated_m_altB)
differences_fB <- calculate_differences(aggregated_f, aggregated_f_altB)

differences_mB
differences_fB

Differences are aggregated with respect to cycles, truncated, since patients have to be counted with integer numbers, and multiplied by 5, since each cycle lasts 5 years.

#Males
summary_mB <- differences_mB %>% 
    group_by(state) %>%
    summarise(
      diff_sum = sum(difference, na.rm = TRUE)
    ) %>%
    mutate(
      diff_sum = floor(diff_sum) * 5
    ) %>%
    select(state, diff_sum)
summary_mB
#Females
summary_fB <- differences_fB %>% 
    group_by(state) %>%
    summarise(
      diff_sum = sum(difference, na.rm = TRUE)
    ) %>%
    mutate(
      diff_sum = floor(diff_sum) * 5
    ) %>%
    select(state, diff_sum)
summary_fB

The previous are the total numbers of years:

The results with respect to the average male or female patient require the previous results to be divided by the total number of male and females patients:

averages_mB <- summary_mB %>%
    mutate(
      diff_sum = (diff_sum)/(n_males)
    ) %>%
    select(state, diff_sum)

averages_fB <- summary_fB %>%
    mutate(
      diff_sum = (diff_sum)/(n_females)
    ) %>%
    select(state, diff_sum)

averages_mB
averages_fB
0.574519 * 12   
## [1] 6.894228

Therefore, in alternative scenario B a male patient gains, on average, about 6 years and 11 month more in the mild stage, about 1 year and 8 months less in the severe stage and about 1 year, as well as about 5 year and 11 months in terms of life expectancy. In the same way, a female patient gains, on average, about 7 years and 8 months more in the mild stage, about 1 year and 1 month less in the severe stage and about 6 year and 7 months in terms of life expectancy.